{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "4f4813f7-f500-49cc-8896-2e434fc7fb8f",
      "metadata": {
        "id": "4f4813f7-f500-49cc-8896-2e434fc7fb8f"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/07_food_vision_milestone_project_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "15052ed2-29a5-4685-a7bd-1dd5f135cafc",
      "metadata": {
        "id": "15052ed2-29a5-4685-a7bd-1dd5f135cafc"
      },
      "source": [
        "# 07 Milestone Project 1: 🍔👁 Food Vision Big™\n",
        "\n",
        "In the previous notebook ([transfer learning part 3: scaling up](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/06_transfer_learning_in_tensorflow_part_3_scaling_up.ipynb)) we built Food Vision mini: a transfer learning model which beat the original results of the [Food101 paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) with only 10% of the data.\n",
        "\n",
        "But you might be wondering, what would happen if we used all the data?\n",
        "\n",
        "Well, that's what we're going to find out in this notebook!\n",
        "\n",
        "We're going to be building Food Vision Big™, using all of the data from the Food101 dataset.\n",
        "\n",
        "Yep. All 75,750 training images and 25,250 testing images.\n",
        "\n",
        "And guess what...\n",
        "\n",
        "This time **we've got the goal of beating [DeepFood](https://www.researchgate.net/publication/304163308_DeepFood_Deep_Learning-Based_Food_Image_Recognition_for_Computer-Aided_Dietary_Assessment)**, a 2016 paper which used a Convolutional Neural Network trained for 2-3 days to achieve 77.4% top-1 accuracy.\n",
        "\n",
        "> 🔑 **Note:** **Top-1 accuracy** means \"accuracy for the top softmax activation value output by the model\" (because softmax ouputs a value for every class, but top-1 means only the highest one is evaluated). **Top-5 accuracy** means \"accuracy for the top 5 softmax activation values output by the model\", in other words, did the true label appear in the top 5 activation values? Top-5 accuracy scores are usually noticeably higher than top-1.\n",
        "\n",
        "|  | 🍔👁 Food Vision Big™ | 🍔👁 Food Vision mini |\n",
        "|-----|-----|-----|\n",
        "| Dataset source | TensorFlow Datasets | Preprocessed download from Kaggle | \n",
        "| Train data | 75,750 images | 7,575 images | \n",
        "| Test data | 25,250 images | 25,250 images | \n",
        "| Mixed precision | Yes | No |\n",
        "| Data loading | Performanant tf.data API | TensorFlow pre-built function |  \n",
        "| Target results | 77.4% top-1 accuracy (beat [DeepFood paper](https://arxiv.org/abs/1606.05675)) | 50.76% top-1 accuracy (beat [Food101 paper](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/static/bossard_eccv14_food-101.pdf)) | \n",
        "\n",
        "*Table comparing difference between Food Vision Big (this notebook) versus Food Vision mini (previous notebook).*\n",
        "\n",
        "Alongside attempting to beat the DeepFood paper, we're going to learn about two methods to significantly improve the speed of our model training:\n",
        "1. Prefetching\n",
        "2. Mixed precision training\n",
        "\n",
        "But more on these later.\n",
        "\n",
        "## What we're going to cover\n",
        "\n",
        "* Using TensorFlow Datasets to download and explore data\n",
        "* Creating preprocessing function for our data\n",
        "* Batching & preparing datasets for modelling (**making our datasets run fast**)\n",
        "* Creating modelling callbacks\n",
        "* Setting up **mixed precision training**\n",
        "* Building a feature extraction model (see [transfer learning part 1: feature extraction](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/04_transfer_learning_in_tensorflow_part_1_feature_extraction.ipynb))\n",
        "* Fine-tuning the feature extraction model (see [transfer learning part 2: fine-tuning](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/05_transfer_learning_in_tensorflow_part_2_fine_tuning.ipynb))\n",
        "* Viewing training results on TensorBoard\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## How you should approach this notebook\n",
        "\n",
        "You can read through the descriptions and the code (it should all run, except for the cells which error on purpose), but there's a better option.\n",
        "\n",
        "Write all of the code yourself.\n",
        "\n",
        "Yes. I'm serious. Create a new notebook, and rewrite each line by yourself. Investigate it, see if you can break it, why does it break?\n",
        "\n",
        "You don't have to write the text descriptions but writing the code yourself is a great way to get hands-on experience.\n",
        "\n",
        "Don't worry if you make mistakes, we all do. The way to get better and make less mistakes is to write more code.\n",
        "\n",
        "> 📖 **Resources:** \n",
        "> * See the full set of course materials on GitHub: https://github.com/mrdbourke/tensorflow-deep-learning\n",
        "> * See updates to this notebook on GitHub: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/550 "
      ],
      "metadata": {
        "id": "IubFUyE5jxPK"
      },
      "id": "IubFUyE5jxPK"
    },
    {
      "cell_type": "markdown",
      "id": "d2b0a6fc-7cbb-43f3-9de5-abe6bd8f53a9",
      "metadata": {
        "id": "d2b0a6fc-7cbb-43f3-9de5-abe6bd8f53a9"
      },
      "source": [
        "## Check GPU\n",
        "\n",
        "For this notebook, we're going to be doing something different.\n",
        "\n",
        "We're going to be using mixed precision training.\n",
        "\n",
        "Mixed precision training was introduced in [TensorFlow 2.4.0](https://blog.tensorflow.org/2020/12/whats-new-in-tensorflow-24.html) (a very new feature at the time of writing).\n",
        "\n",
        "What does **mixed precision training** do?\n",
        "\n",
        "Mixed precision training uses a combination of single precision (float32) and half-preicison (float16) data types to speed up model training (up 3x on modern GPUs).\n",
        "\n",
        "We'll talk about this more later on but in the meantime you can read the [TensorFlow documentation on mixed precision](https://www.tensorflow.org/guide/mixed_precision) for more details.\n",
        "\n",
        "For now, before we can move forward if we want to use mixed precision training, we need to make sure the GPU powering our Google Colab instance (if you're using Google Colab) is compataible. \n",
        "\n",
        "For mixed precision training to work, **you need access to a GPU with a compute compability score of 7.0+**. \n",
        "\n",
        "Google Colab offers several kinds of GPU. \n",
        "\n",
        "However, some of them **aren't compatiable with mixed precision training.**\n",
        "\n",
        "Therefore to make sure you have access to mixed precision training in Google Colab, you can check your GPU compute capability score on [Nvidia's developer website](https://developer.nvidia.com/cuda-gpus#compute).\n",
        "\n",
        "As of May 2023, the GPUs available on Google Colab which allow mixed precision training are:\n",
        "* NVIDIA A100 (available with Google Colab Pro)\n",
        "* NVIDIA Tesla T4 \n",
        "\n",
        "> 🔑 **Note:** You can run the cell below to check your GPU name and then compare it to [list of GPUs on NVIDIA's developer page](https://developer.nvidia.com/cuda-gpus#compute) to see if it's capable of using mixed precision training. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "5daf90cd-d153-4193-8e4c-aab705679323",
      "metadata": {
        "id": "5daf90cd-d153-4193-8e4c-aab705679323",
        "outputId": "1abf993d-f028-42c2-e55b-19dd863ac82f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: NVIDIA A100-SXM4-40GB (UUID: GPU-269f6413-0643-12da-9e68-ef2cb8b4aad3)\n"
          ]
        }
      ],
      "source": [
        "# Get GPU name\n",
        "!nvidia-smi -L"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c25cc91-11e8-442b-9d9f-37ac32e97dbb",
      "metadata": {
        "id": "0c25cc91-11e8-442b-9d9f-37ac32e97dbb"
      },
      "source": [
        "Since mixed precision training was introduced in TensorFlow 2.4.0, make sure you've got at least TensorFlow 2.4.0+."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "2bcbb711-ed02-4616-830b-b13212215eaf",
      "metadata": {
        "id": "2bcbb711-ed02-4616-830b-b13212215eaf",
        "outputId": "f82902f9-937f-47a9-a09a-87f10b131802",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.14.0-dev20230518\n",
            "Notebook last run (end-to-end): 2023-05-19 02:54:07.955201\n"
          ]
        }
      ],
      "source": [
        "# Note: As of May 2023, there have been some issues with TensorFlow versions 2.9-2.12\n",
        "# with the following code. \n",
        "# However, these seemed to have been fixed in version 2.13+.\n",
        "# TensorFlow version 2.13 is available in tf-nightly as of May 2023 (will be default in Google Colab soon).\n",
        "# Therefore, to prevent errors we'll install tf-nightly first.\n",
        "# See more here: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/550 \n",
        "\n",
        "# Install tf-nightly (required until 2.13.0+ is the default in Google Colab)\n",
        "!pip install -U -q tf-nightly\n",
        "\n",
        "# Check TensorFlow version (should be minimum 2.4.0+ but 2.13.0+ is better)\n",
        "import tensorflow as tf\n",
        "print(f\"TensorFlow version: {tf.__version__}\")\n",
        "\n",
        "# Add timestamp\n",
        "import datetime\n",
        "print(f\"Notebook last run (end-to-end): {datetime.datetime.now()}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d80666c6-53e0-433c-996e-9094c6a4cc29",
      "metadata": {
        "id": "d80666c6-53e0-433c-996e-9094c6a4cc29"
      },
      "source": [
        "## Get helper functions\n",
        "\n",
        "We've created a series of helper functions throughout the previous notebooks in the course. Instead of rewriting them (tedious), we'll import the [`helper_functions.py`](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/extras/helper_functions.py) file from the GitHub repo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "5558dbb4-90ca-49a3-856d-6cfe063d3df6",
      "metadata": {
        "id": "5558dbb4-90ca-49a3-856d-6cfe063d3df6",
        "outputId": "a8d12aae-1687-423e-b455-aeb3c4ae7934",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-19 02:13:56--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10246 (10K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "helper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-05-19 02:13:56 (100 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Get helper functions file\n",
        "import os \n",
        "\n",
        "if not os.path.exists(\"helper_functions.py\"):\n",
        "    !wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
        "else:\n",
        "    print(\"[INFO] 'helper_functions.py' already exists, skipping download.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "02363842-bbc3-482d-86c2-90c7d093f7fa",
      "metadata": {
        "id": "02363842-bbc3-482d-86c2-90c7d093f7fa"
      },
      "outputs": [],
      "source": [
        "# Import series of helper functions for the notebook (we've created/used these in previous notebooks)\n",
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, compare_historys"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "87e737e5-e729-4492-8fa2-7f64f376164f",
      "metadata": {
        "id": "87e737e5-e729-4492-8fa2-7f64f376164f"
      },
      "source": [
        "## Use TensorFlow Datasets to Download Data\n",
        "\n",
        "In previous notebooks, we've downloaded our food images (from the [Food101 dataset](https://www.kaggle.com/dansbecker/food-101/home)) from Google Storage.\n",
        "\n",
        "And this is a typical workflow you'd use if you're working on your own datasets.\n",
        "\n",
        "However, there's another way to get datasets ready to use with TensorFlow.\n",
        "\n",
        "For many of the most popular datasets in the machine learning world (often referred to and used as benchmarks), you can access them through [TensorFlow Datasets (TFDS)](https://www.tensorflow.org/datasets/overview).\n",
        "\n",
        "What is **TensorFlow Datasets**?\n",
        "\n",
        "A place for prepared and ready-to-use machine learning datasets.\n",
        "\n",
        "Why use TensorFlow Datasets?\n",
        "\n",
        "* Load data already in Tensors\n",
        "* Practice on well established datasets\n",
        "* Experiment with differet data loading techniques (like we're going to use in this notebook)\n",
        "* Experiment with new TensorFlow features quickly (such as mixed precision training)\n",
        "\n",
        "Why *not* use TensorFlow Datasets?\n",
        "\n",
        "* The datasets are static (they don't change, like your real-world datasets would)\n",
        "* Might not be suited for your particular problem (but great for experimenting)\n",
        "\n",
        "To begin using TensorFlow Datasets we can import it under the alias `tfds`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "ef4f160e-55f6-47e0-8c4e-fba7d1dfd91b",
      "metadata": {
        "id": "ef4f160e-55f6-47e0-8c4e-fba7d1dfd91b"
      },
      "outputs": [],
      "source": [
        "# Get TensorFlow Datasets\n",
        "import tensorflow_datasets as tfds"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6fbb4d29-aeb1-4ed5-928e-334cc3637b13",
      "metadata": {
        "id": "6fbb4d29-aeb1-4ed5-928e-334cc3637b13"
      },
      "source": [
        "To find all of the available datasets in TensorFlow Datasets, you can use the `list_builders()` method.\n",
        "\n",
        "After doing so, we can check to see if the one we're after (`\"food101\"`) is present."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "1aeb0578-b57f-4993-a834-8246aca0e252",
      "metadata": {
        "id": "1aeb0578-b57f-4993-a834-8246aca0e252",
        "outputId": "f849dd9d-9853-481a-a472-4e2f01a710f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'food101' in TensorFlow Datasets: True\n"
          ]
        }
      ],
      "source": [
        "# Get all available datasets in TFDS\n",
        "datasets_list = tfds.list_builders()\n",
        "\n",
        "# Set our target dataset and see if it exists\n",
        "target_dataset = \"food101\"\n",
        "print(f\"'{target_dataset}' in TensorFlow Datasets: {target_dataset in datasets_list}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9f9c2ba-5386-4eca-a26e-1fb564287faa",
      "metadata": {
        "id": "f9f9c2ba-5386-4eca-a26e-1fb564287faa"
      },
      "source": [
        "Beautiful! It looks like the dataset we're after is available (note there are plenty more available but we're on Food101).\n",
        "\n",
        "To get access to the Food101 dataset from the TFDS, we can use the [`tfds.load()`](https://www.tensorflow.org/datasets/api_docs/python/tfds/load) method.\n",
        "\n",
        "In particular, we'll have to pass it a few parameters to let it know what we're after:\n",
        "* `name` (str) : the target dataset (e.g. `\"food101\"`)\n",
        "* `split` (list, optional) : what splits of the dataset we're after (e.g. `[\"train\", \"validation\"]`)\n",
        "  * the `split` parameter is quite tricky. See [the documentation for more](https://github.com/tensorflow/datasets/blob/master/docs/splits.md).\n",
        "* `shuffle_files` (bool) : whether or not to shuffle the files on download, defaults to `False` \n",
        "* `as_supervised` (bool) : `True` to download data samples in tuple format (`(data, label)`) or `False` for dictionary format \n",
        "* `with_info` (bool) : `True` to download dataset metadata (labels, number of samples, etc)\n",
        "\n",
        "> 🔑 **Note:** Calling the `tfds.load()` method will start to download a target dataset to disk if the `download=True` parameter is set (default). This dataset could be 100GB+, so make sure you have space."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ccd73b2-37df-4402-afc5-49de4dea4c77",
      "metadata": {
        "id": "5ccd73b2-37df-4402-afc5-49de4dea4c77"
      },
      "outputs": [],
      "source": [
        "# Load in the data (takes about 5-6 minutes in Google Colab)\n",
        "(train_data, test_data), ds_info = tfds.load(name=\"food101\", # target dataset to get from TFDS\n",
        "                                             split=[\"train\", \"validation\"], # what splits of data should we get? note: not all datasets have train, valid, test\n",
        "                                             shuffle_files=True, # shuffle files on download?\n",
        "                                             as_supervised=True, # download data in tuple format (sample, label), e.g. (image, label)\n",
        "                                             with_info=True) # include dataset metadata? if so, tfds.load() returns tuple (data, ds_info)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "51033691-941b-4659-80df-9461689753f0",
      "metadata": {
        "id": "51033691-941b-4659-80df-9461689753f0"
      },
      "source": [
        "Wonderful! After a few minutes of downloading, we've now got access to entire Food101 dataset (in tensor format) ready for modelling.\n",
        "\n",
        "Now let's get a little information from our dataset, starting with the class names.\n",
        "\n",
        "Getting class names from a TensorFlow Datasets dataset requires downloading the \"`dataset_info`\" variable (by using the `as_supervised=True` parameter in the `tfds.load()` method, **note:** this will only work for supervised datasets in TFDS).\n",
        "\n",
        "We can access the class names of a particular dataset using the `dataset_info.features` attribute and accessing `names` attribute of the the `\"label\"` key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "6115265e-469b-4fcd-a54b-7fb4e59b90af",
      "metadata": {
        "id": "6115265e-469b-4fcd-a54b-7fb4e59b90af",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5137dbb-54fa-4bf8-fcb3-7d1d21d2ec81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FeaturesDict({\n",
              "    'image': Image(shape=(None, None, 3), dtype=uint8),\n",
              "    'label': ClassLabel(shape=(), dtype=int64, num_classes=101),\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Features of Food101 TFDS\n",
        "ds_info.features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "51532ab7-9ffa-4d19-9726-389984f39a04",
      "metadata": {
        "id": "51532ab7-9ffa-4d19-9726-389984f39a04",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82996ef0-333a-451d-fba7-446dbe8b0329"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apple_pie',\n",
              " 'baby_back_ribs',\n",
              " 'baklava',\n",
              " 'beef_carpaccio',\n",
              " 'beef_tartare',\n",
              " 'beet_salad',\n",
              " 'beignets',\n",
              " 'bibimbap',\n",
              " 'bread_pudding',\n",
              " 'breakfast_burrito']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Get class names\n",
        "class_names = ds_info.features[\"label\"].names\n",
        "class_names[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "896c2153-9420-432d-a424-704424789ba8",
      "metadata": {
        "id": "896c2153-9420-432d-a424-704424789ba8"
      },
      "source": [
        "### Exploring the Food101 data from TensorFlow Datasets\n",
        "\n",
        "Now we've downloaded the Food101 dataset from TensorFlow Datasets, how about we do what any good data explorer should?\n",
        "\n",
        "In other words, \"visualize, visualize, visualize\". \n",
        "\n",
        "Let's find out a few details about our dataset:\n",
        "* The shape of our input data (image tensors)\n",
        "* The datatype of our input data\n",
        "* What the labels of our input data look like (e.g. one-hot encoded versus label-encoded)\n",
        "* Do the labels match up with the class names?\n",
        "\n",
        "To do, let's take one sample off the training data (using the [`.take()` method](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#take)) and explore it. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "60b89a61-50e3-4058-9dae-bb0cefacca16",
      "metadata": {
        "id": "60b89a61-50e3-4058-9dae-bb0cefacca16"
      },
      "outputs": [],
      "source": [
        "# Take one sample off the training data\n",
        "train_one_sample = train_data.take(1) # samples are in format (image_tensor, label)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "42bb1dfd-7c88-49a1-a174-1f6ba973ad61",
      "metadata": {
        "id": "42bb1dfd-7c88-49a1-a174-1f6ba973ad61"
      },
      "source": [
        "Because we used the `as_supervised=True` parameter in our `tfds.load()` method above, data samples come in the tuple format structure `(data, label)` or in our case `(image_tensor, label)`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "8a13d601-0911-4a36-be2c-a59ca6b809a4",
      "metadata": {
        "id": "8a13d601-0911-4a36-be2c-a59ca6b809a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67829127-b7c7-4bfa-b778-15921cf6a6c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_TakeDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# What does one sample of our training data look like?\n",
        "train_one_sample"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a83d5488-6cd0-4dd2-8d83-ae80eaf06c73",
      "metadata": {
        "id": "a83d5488-6cd0-4dd2-8d83-ae80eaf06c73"
      },
      "source": [
        "Let's loop through our single training sample and get some info from the `image_tensor` and `label`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "123d5880-776f-4002-8eca-5f1fae500b7a",
      "metadata": {
        "id": "123d5880-776f-4002-8eca-5f1fae500b7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "462b4610-e365-414b-a880-78a1bf333d44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  Image shape: (512, 512, 3)\n",
            "  Image dtype: <dtype: 'uint8'>\n",
            "  Target class from Food101 (tensor form): 90\n",
            "  Class name (str form): spaghetti_bolognese\n",
            "        \n"
          ]
        }
      ],
      "source": [
        "# Output info about our training sample\n",
        "for image, label in train_one_sample:\n",
        "  print(f\"\"\"\n",
        "  Image shape: {image.shape}\n",
        "  Image dtype: {image.dtype}\n",
        "  Target class from Food101 (tensor form): {label}\n",
        "  Class name (str form): {class_names[label.numpy()]}\n",
        "        \"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ab240eb4-fedf-42d5-aaba-8906d11af3e2",
      "metadata": {
        "id": "ab240eb4-fedf-42d5-aaba-8906d11af3e2"
      },
      "source": [
        "Because we set the `shuffle_files=True` parameter in our `tfds.load()` method above, running the cell above a few times will give a different result each time.\n",
        "\n",
        "Checking these you might notice some of the images have different shapes, for example `(512, 342, 3)` and `(512, 512, 3)` (height, width, color_channels).\n",
        "\n",
        "Let's see what one of the image tensors from TFDS's Food101 dataset looks like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "5bc3ed60-3003-4239-ad3b-9a77b4e750f0",
      "metadata": {
        "id": "5bc3ed60-3003-4239-ad3b-9a77b4e750f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "343bcedc-3853-4c52-b27e-9f69e1ee8941"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(512, 512, 3), dtype=uint8, numpy=\n",
              "array([[[ 12,  13,   7],\n",
              "        [ 12,  13,   7],\n",
              "        [ 13,  14,   8],\n",
              "        ...,\n",
              "        [ 21,  11,   0],\n",
              "        [ 21,  11,   0],\n",
              "        [ 21,  11,   0]],\n",
              "\n",
              "       [[ 12,  13,   7],\n",
              "        [ 11,  12,   6],\n",
              "        [ 11,  12,   6],\n",
              "        ...,\n",
              "        [ 21,  11,   0],\n",
              "        [ 21,  11,   0],\n",
              "        [ 21,  11,   0]],\n",
              "\n",
              "       [[  7,   8,   2],\n",
              "        [  7,   8,   2],\n",
              "        [  7,   8,   2],\n",
              "        ...,\n",
              "        [ 22,  12,   2],\n",
              "        [ 21,  11,   1],\n",
              "        [ 20,  10,   0]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[188, 191, 184],\n",
              "        [188, 191, 184],\n",
              "        [188, 191, 184],\n",
              "        ...,\n",
              "        [243, 248, 244],\n",
              "        [243, 248, 244],\n",
              "        [242, 247, 243]],\n",
              "\n",
              "       [[187, 190, 183],\n",
              "        [189, 192, 185],\n",
              "        [190, 193, 186],\n",
              "        ...,\n",
              "        [241, 245, 244],\n",
              "        [241, 245, 244],\n",
              "        [241, 245, 244]],\n",
              "\n",
              "       [[186, 189, 182],\n",
              "        [189, 192, 185],\n",
              "        [191, 194, 187],\n",
              "        ...,\n",
              "        [238, 242, 241],\n",
              "        [239, 243, 242],\n",
              "        [239, 243, 242]]], dtype=uint8)>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# What does an image tensor from TFDS's Food101 look like?\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "808e21e5-6951-4eac-bb3c-00fc1ae3fa0f",
      "metadata": {
        "id": "808e21e5-6951-4eac-bb3c-00fc1ae3fa0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b70610c-2b0b-4e32-ea4a-da603ed7091f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<tf.Tensor: shape=(), dtype=uint8, numpy=0>,\n",
              " <tf.Tensor: shape=(), dtype=uint8, numpy=255>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# What are the min and max values?\n",
        "tf.reduce_min(image), tf.reduce_max(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ff7dad2-0972-4905-b14c-549609401d98",
      "metadata": {
        "id": "3ff7dad2-0972-4905-b14c-549609401d98"
      },
      "source": [
        "Alright looks like our image tensors have values of between 0 & 255 (standard red, green, blue colour values) and the values are of data type `unit8`.\n",
        "\n",
        "We might have to preprocess these before passing them to a neural network. But we'll handle this later.\n",
        "\n",
        "In the meantime, let's see if we can plot an image sample."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "630fb994-b61f-4a03-8831-83fe50ab6ca1",
      "metadata": {
        "id": "630fb994-b61f-4a03-8831-83fe50ab6ca1"
      },
      "source": [
        "### Plot an image from TensorFlow Datasets\n",
        "\n",
        "We've seen our image tensors in tensor format, now let's really adhere to our motto.\n",
        "\n",
        "\"Visualize, visualize, visualize!\"\n",
        "\n",
        "Let's plot one of the image samples using [`matplotlib.pyplot.imshow()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) and set the title to target class name."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "3f19d6a3-b2ac-47e9-a670-c08461897d6c",
      "metadata": {
        "id": "3f19d6a3-b2ac-47e9-a670-c08461897d6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "6cf08460-08e4-4a5e-c039-3598fb992f2b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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qkvq1X/u19n/+n/9n+9rXvtbmeW5f85rX2B/5kR/ZuPeTn/yk/YZv+Aa7t7dnR6ORfdOb3mR//Md/vHfPNpdUa6398Ic/bN/ylrfY8Xhsd3Z27Nd93dfZX/mVX9l4x//6v/6v9g1veIPNssw+9thj9u/+3b9r//yf//N2NBr17gO2uig/8sgj9p3vfGfv2vPPP2+/9Vu/1T700EM2TVN769Yt+6/9a/+aff/739/e8773vc/+/t//++3h4aHN89w+9thj9ju+4zvs2dnZS27rCr54QFgb6Qyu4HcUvP3tb+fBgwdbRfvfTPixH/sxvv7rv57/4//4P3jLW97yW/quK3Dwjne8g1/+5V/m4x//+G93V67gdxhc2RSu4CXBer3ufdda8573vIednR2+7Mu+7LepV7+zYTjnH//4x/mJn/iJLzh9xxVcwQvBlU3hCl4S/Ef/0X/Eer3mzW9+M2VZ8sEPfpB/9s/+Gd/93d/9eb2Hfrvh/v37G3mXYsiyrBdM9v8WeOUrX8m73vUuXvnKV/LZz36W9773vWRZxl/4C3/ht7trV/A7EK6IwhW8JPjKr/xKvu/7vo8f//EfpygKHn/8cd7znvfwbd/2bb/dXfu88BVf8RWXBpSBM3qHRHr/b4J//V//1/kn/+SfcPfuXfI8581vfjPf/d3fzate9arf7q5dwe9AuLIpXMH/Z+Bnf/ZnN1QxMezv77duuldwBf9fhSuicAVXcAVXcAUtXBmar+AKruAKrqCFF21TmM3zNjjJGIu1FmMMxhisARD+mvWFTmR7vxACpRRJkpCmKaPRiDzPGY1GpGlKmo3a35IkQUgAi1IKKWkLpwgBQkCiVC8wSAiBlBKlFNalA2//xlqsBYsTiKSU7T8bPR/3NQ7CsgL3Uv9saF8oiUIiI7ra/tb2t2sz/B76Gr6DizI1xvS+WywG03s+fia+LoTAYHrthvdIBgFl1qINWKS/32DRg/YkAkl4lZAWYzRSynbN2zn0Y+7mDqRyY7dRmghjNEJAU5ecnp5wfn7KfD5jd+8aaTruvdsaN9cGkD7a2hhLoiSCyv2mLVIoBApr3bu1NSAAKTBYsBohuvWI+9nvs/uu2NwH4beQ5yfMr5ESqRLCJEkpUUK2cxzPevzstrUb7hFj3W5NldroK4DV/X1hrIFov9moT1rrdr3CbxI7WL92GL7/AhD+rAl002CtQQjpznei/P4SNE2NEJblasnTz3yG8TQhSTXni/tcFA+omgX5GFbVEpWmZPmYsmpQSY4SilSknB2teORlr+bxR19LxoyL0wJMOKMaQ43BkpL09n/Ye/E+bHESEKZdSoeLtNYYbRB+7owx1HVNXdfopqGuG+qqoSgKiqKgqioWi4X/Xrb3VlWFtab3vnidYtxkreuHNvgzbpDS7yt/7tI0Jc8z1ydtqMqKuq7bfSOEwwlKAC3WsmANwloEbr8JBA7T+TNpafdhQCH3Fp9fMfSiiUKMtAJRCOvjCEFYAIGUCiFkSwiyLCPPc/I8J8sy0jRtP9M0RcikJRwqcc8JPwFh87kN796jog0Nm0g3XJNSYsPEDqJLpZSYLYs4RCDx9W1/gyFMvSNa3d+h/92nu+YOWEfkrDXt2No2hUCKPpIfvj++pqTaQArb4ouFP+zuFttdFd1z1hp/gyfsJlzvEGw4aJv9se0B7RFnv2HruqGqa+rGfTZNTaISrFDtARdIQCAFCD+/SgqkpGVAhBBYI1pi359fQLgTMZy3eC43CKwQ3ZFzsWDdFPkJDWvi+JYBgqKbn/a6RxpxH8N+2xYBLoRokdaQaQmILJ5vay2ynWM29u2QAZFCIKM9Hhg5a/vnwL8ArY3fn+47sutz0ziCkySSPM+5fu0G090MIWpkomlOVzSrFRbDeDyhqmustUwmE9JsTFPVKBRJolitFhhTsy5XjoiJ1G8Zx2AqAcL2mY0hsY73pRtPn6AqpVBS+jPQzaWUEpHnaK1pGs1sNkVrt25FUVCWFev1mvV6zXK5ZLVaUVVV+88R3o6xiucVAkHozrkxfh8oz0woiVIJSkmMMhjdMV6uLeP3lztDYSkcjqC3Rx1D53+3ft3D8y/SUPCiiUJZNl1nIgiIMHAejuPPSNOOEIR/SZL4exRSKk80FIika0N1RMBa3eOswSKlQA0OVfhUnrPSgZtVqsdB9Q5hhxkvRRItAhm2IcI6OGS35Wy317rfHJKJF8eNS0REouuHtRYr+ggh3nBD5GP8wvcQhmusNyaEJww2RpbR7+GRFnlIjF+HANsIQtxY75Lt5lJrQ1U3rNcFi8USYyFPx2BBJSlCSEAhhPFcjkAYT1AEWCNoGo2UeCkheo01CGE9kQhEMRCjuHt9xNKb93g8Im7bIgJj4pH2YGoZDNntW8AM9k5MVIENJA8OcQdpwXH2XX4qIYSjSEGKC5wF9BBgvFZA64orpFvXzTnpf28Jb9ym6JgHh7CMH4Oby9lshlKWNE3Z2dljWZyyLE6o65pxlnfzIt0AtK5RUjAaZxyfPOBzn/ss8/w6eTpFkHiEbwAJwna4bxuD5BGoFN0etohNghxJg2HuAyF2+MhpPhyHb8jzHGNsK1WUZUlZlqxWjkCs145ArNdryrKkaRqapvFtm5aB7hivwFyB1oG4hV3TEa+YQGvdOOYCt6fDM0JIDBYpTEvowyQp5ebYWovVHd54MfCiiULvAAhaxC1FgsoUeZ73VENOJHKSQSAG4V9M0ZVSWCF7v7v3WS9x9JFfQGy97x6M3xTCHyrw6oeIc90mCVw2WQGxbkMkBjuYk+1c3/Az3qRDLi4mCvE4N1U0m5JRaDee442Fa/tFe8jcWe8OePdcx3kPxxdzrRtgLVYYlEqw1m18gEY3lGXBxcUFJycn3H/wgOzsjHJdsruzy2QyIc9zxuOxz8kkemsnhEBrizbWSxeWRKaeG/QHXIVxWRDWSxyff21irnMoBQ3v7+Z8uLbdWrb3SIn17Q3natt69gh/dI8GZEw8pMSaTuezra3hGDsk49Qow/f250l6Hsa6DaKkk6I8QgtcrGtXIYRF1w5l6dqQpglpkiOkQqmUslpSlAX5eESW5xgEwkvMy9U5TQEXFzVPP/MbPHw7Z5RNHOEPTBMWKzyfHPZzmKto38frp7XBmk61FO6RQri3e8IWxm0jFRuA0QatLVmWtSoirXMmkwlaG5pGtyqmuq5ZLBasVivKsmS9XrFeF60aCtu05ysQBM/n9NZca+3mN8i/sVTb9iwohCN8JhNkolCtCl+3bJFw4q2no7/JRMFNKi2nr1RCmqSMRuOWGLjPMWnqmk3TdIMYxP+UctICLRfSqVQIWrKNjR6Y/E39fKxjdP11m2DISYXvLbdG15awFiH9eYsofKzeiemSsaGnnYrE39VRdX/NBJWE64w/0zE/698nRcupX6YWcP3ouNqgphkSjXhjhVkNhM5ioqvDxg3WOuLq8Jvc4LqGai03AoOSHceLktR1TVGUnJyd8Pzzz/HgwQNOz85QUrBerjmbnTOdzphOp+zv7zGdzsiynCzNWqkQAY02FJXT7WZZziQPSKmbcb8q7TPCymhJ/B1WdOsr/Qm1tLr08NnNucBtCq8CCIc65tb92sVIKMyBVQo1QPzh71gt27YlIhVSUEWA1x93XbLdxnHvis5CvFYt8zVAqF1f+rahVgXn9dNBKrDRC621XhUD1jpOtixKalMj1QSExBqBFClCKCf5WEuSKhptaeoaKSxgKMsKpRRaVwg8UhMaCPYk421mqjem8PfG/EV/h33bI5z071FKYcDv9TBfoIzBGFA4O09iunPc1KYlGNoYZrMZddNQ13WrYloul/7vJVVdUVcVdePmKqhWw7/AVHTjA20s1jgVXcyiBbwh/LY1rlP+XFtswDnWkxDpVy16xwvBiyYKo1Hq7QM5ozwny0fkmTMWK5WQpsEu4I3FokMYwQg8XKyWKMhIvLEWRGczCJPRodtO1I03R3t/tNmN6QxwgSiZiNNpiUIw1EkJrW4w/G8Ig5MY4ZSYEgth2wXsIc6I+A3bCYvqFi822l8uVfgZQUjhdZjd4TbGRIbm7lO0Z9+2fQlMYdsti99YtrXHxAdwqKpqrxGM0S61shSSqq44PTvh6Wee5umnf4PVakmja6SQrJZrzs+W5HnGaDzm4OyA/b0Drl27zmxiSZO05UbLquLk5ISiLJjP5qgdJ6FucvYdwQ3IPSCDVuIjqNdiBmNTMm0RjokIhOfAjI0Oa5gvOqQct6WiOYrbNjFCc4vW7pGwMCJmDISIEIRtEVw8BzFztOEU4I2wPYlmAEHFaa1pnT7CGF23uj1smgZtGqqy4uL8nHsPjrh555DZrnIctTFOuqsr6qYiyRLSLEcK0NZ4VYlAC6ibCmNrLA1CBpuA31+eQRlKvttUqd31ThsRiGQY15DJUkmCjAifkAKpZFiOVho1xqAQzgCfpu21NPp7Pp/TNE2nalovWa+XLBZLlssF63XRGpKDFmVDEpcSqzXaBIknXvduzwmccZrG7UUpnPo9SCL43WrF5txdBi+aKBwcHLS2gTTpDMbuMCUDFVFnF3ghcbYV34KXjT+0rdFU9FUnyreN7K7HOtkW8UdGmlY8i8bSeiN4YtAhXNPqJEP/zODggje6C7wo2h2U7WqomFB0nzEX2HFxsWTStyN0m7qTanoIxoQ2u7V3RqcB4jadbraVKI3xXJu7IKzsnpcO+cSieWgvHnNLiK1TIySJ5/Jsg7YNy9WSk5NjlsuFkyZSbxAzglo3NGvNuiwpy4piVWE01Ht7TEYT0tR5nVyslhwd3WexXLoDJZ2jglLKS1eet7V+zkSHtIeIftscht+22Rs2VjWSSIfXhRAkSdJ7b49jjTndwdoGpO8IzoCQ+X087OPQMwpcbe6YQx6qx4IKKUDcjkA6JsM6W4oVpmdvkVKCsWij0bqh0TXr9YrT0zOef+45Ts7usXOQs25OaFihxgnaNNSm4uz0jL3dPYTT9Tlkay11XZErG/FJFmM11htllXJIm7gPg30Ynym3tt18dPNv2iPZe1Z6RsHSvtPdE86kiIirEyZjb7zA9GqtSdMUrbVTNRmNMftUVclqtWK9XrNYLFgul87bqa5pGmevdSoqvbmmWGeoHjgyhPPe4gqc1iOsu6BzKsCrVF8MvGiicO3wujuASYIUzqDsxB7Vehx1ahvhJnnAEXffnSpKtFgpcOaeKrdEoi9dhLu905W/34n2Tu+IVwdIf/AcoaEnGnupQooWe7aIzesvYyOgHSxCuN9Y4/g0P56hmmf4TDf+PpLZJgYPucy43W3fbasGuZw4hU0W3C6damoLUvDssxCyJeyNbjb6G38fqiukSICgnvIeY4mXfmjIRwn5KHPeHpX18q8zPC5WFzRNQ1EVnJ3vMZ/OyfMRWMFqveDo9D5lVaKUJEtSJtMRCEui0ogR6SyxMfKOGYwhpxwQMmHPRNKf9dKS6BYbv/02tLRt+4HwezGegZroMpWHu9AxSFi8e21kW8HZmrftnWG729S2gk27SYwggwQSq1kAr4bwOnnv6qq1Zr1ccXx0zIN791hcnGOWDY0dY5IVZCVZakE6G0JRFiwWC+bTHaSU1FVJVdZoLQZILEitXjKTsjcHVnRzLxOF0bpjhqRzxXYG403PvZiIdHMm/F4VCBWIsWwRNtCqr6y1NNY4dbqSQCBAhqYRJIn0xNt4O4GhaTImkxFa29Zrab1eUxQF63XRGqrLsqSqK5y7b995xFiLQnpPJoszudhOcS2Ec5EXrbLBERTPcL5ImvBS1Efj1sU0UExHHeVg80l3tmSXeCxwxpubtxu0EB0nHcRXGW16oD1g7QH2jQsvZjp85jiY7sB0hyKA9UgvRhLd/R2xcgckejYcKgvWRuLcJWLZ8NB27nIvHM/gn750LYZIRErpDslgLK6Vftsi7KB4rv28tUgsGlcnaW0inXg8saFbhLUIqhspGI9zZjtTZusxaZ4wGqU0usJq1xndWJq6oWkstV7z4HjNYnFGno/J0xECQVWXVGaFlIKjI4MSgtFoxP6+QiC9YRdskD2tQbUHeRsS7YzGoZ9h1hxn7NYZEdQYfkal2wPC2m7+6O9TwdCZYTth3yaJWEvXl0Ag5KA90TE18R4aSi+be7Nbxw4puusdNx3alFjhvV4C19ZRS6y1rFYrnrt7l+fvPsf5+TnT6Zjd/SnZXHCyXKMxrIs1Se7epbUz0OaZI/Rl2VBXGmtSAjYbOku0ayVlO2a/OJ7e9t3OQ19lrJZu10j2bHXd/YEFAALD19EKAisaxp4kntEI6lrj9k+mEi+NWxcT4fejUoI0TTDGknv311DKtKrq1uV1tV5xfnFOPspZL1dUVe3jI5yxOpYUrRHefmAcOyuk54dFy+AGSYLo4/PBSyAKI4To3KU6JN/nQoL+181of9NexjV3RMFzBe19Xk4j/mq3thFDTKSCLji2H2hrUb0Dhtvror9BAjvY+sKH/0vv2mbdQzFiADZc/kKftnEtwxiJtk+2P77LYim6eyJC2bbRVxkECQeLQ6BtpzeRS/jxMqnlMoTW/k5HXJWUjMY5O7tTLlYTpDQkKcjEG289UchyhdbQVIblomBZVKzWC1KVIWUCGLKxc2cuihVHxw/I8zFpkjKbCXI5wnp1V924g5QoZ3cIuuvOthXGLKPv2yS5bu06fOQYH6zeKi0ExuWy+RnO1TaJ0JoB40D/vMTE/IW82IbSETbEEsWxPv1xh1gMhGgl8+5IeHuDMazXa46Pj7n73HNcXJyTpgn7B7vs7k1Z1CdcLC7IpgaEoWkMaepc0K21lEUJVtI0zuVUSheEGIgSQd1ho7PIEGc4TyqtdU8dHcYZz0n3DL3v7fwHh3/b2QIt/RiQYKOEyN4BHnF0x8+ITspJpEJY1Z7FMD5roa5rACYTmEwmTkqoKg4OD1iv11xcXLBaBIP1iqqsMX68LjYCTwQ6NaiJmJVOTebvuWwTDuBFEwVnPI7FTsdJhEUO3909oRPdJh36ZfcRW7xQwd01eLt0hMIRPbt1cLFOMbxTeClCEG+OPgcR7une3blnSu9SGLiBdqIFbhMFsaLbRW5zqICAPUESsVuiJzSeGxTKuxcGXBzmiE1EIESna9zkAGUfQVkCrwBBV2qF46itRRjbRh4HqSgoDDqE49trccSm2B2+t/vCOo7NYJyxDkmSJmRZilICIRySCDEkxjZIATIVZCpHCEWxqpEK1uuKptJo6yJIZSKYTCd+HirW6xV3n38OpRKuNZrd/QOSNKUxmovFgsXFBVmWcnBwwGQy2dgf8TwKEZ3qLbBBFOkYr8BD2+g+E+9Dr4PflFQGUt3wIETvM0FiiPZCuydF55oZtx/idjadMIaG6cF+8oMJXj/GM2LCgtXOWK2rmpOTE56/e5fT0zPSVHFwsE+epxTlCisMaeJiTrQ1CGuwSPIsR1hJWVWYRmI0KJkiRNLGAmitMcrFnYSzH6/XkFkariV0hGyID4b39XHQ9rXoM2bdKQvPiE5o60mgrWeRP1+6iQMZVceoedfZNE29usfNw3q1dja2omB5sWC5XDu7xGpNWbjrBucNZ6zxDLAgqI2gY1AvUWZshRdNFMLmiiWFjnJ3nL3wkaTINvzUcWMibMzWP6qd0O4lYYNGZyI6q9KLYs6zxx8Qj71UhJSs6K63omJAcP7dJkRpyq4PVgSTt/RjUYDFGt9vG+mIrSc4LTJ345YiCjIKA4hUCe2G7AlAUf8GsMnp9BFyd49oEYSUsmtfgtHaeSY5bOCIAn31QYiEdMRsQIhskJhoibOQkRjuEarvjUeIXoQVTnKUMsFot9mT1LmXysQiE4lQkixLmU1m1GWDERqVjlC5oipqMBZjGlQikSMnAZAZdKk5W1TIu1BVFY02TCZTGmM4Ojri9OSEyXhEIiSJUJA7zk9EtodWf96e7IDuA0LqLgVVnLUWTRdfGvgC6Paq8OrF8M8QPOnCDXjhLkI+0dvddupLbu17AqIL+8z/1p3FeBybe8WKgSePf65lWghI0no9uh+5ASsN2kBRFpwcn3B+foHRlvFswv7uISoXnJw9TyM049EMLVe4IK4a2whUliCRaF1RFgV1DdKm6Lqm0iUX5+dM8z0SOUKgECqFsFY28qYzYQ4Gtg9Ld26tQamOi7bggwKDEdbhCmMtwkRrF+G0ILH3iarsMWeXqYM78AGDSrQ4DpzLvjYuVYVUEmUTpBDUPso/TZy6SWtNfbBPUZQsFytW6xWr5YrzswXrYuXdXV2qDhNJtIG5HqrQPh+8aKIQxxv0VRl9ScC2MpUgsJit2UoIBuvnkG2U4yfwtiGi0x0E2xkRIRLb4tZpZzt4BbWcgl9jAV7nbFokR7f+ngPtNoWLiHSWf6U6/bl7THj3Qes54vbqgDvpz1X3t+coBz7UYa5j8XcblxRvxl5ULBF30K5HkLK6+fdHv32/UtJzHLb1147BRm22cxUNqh+c5Z5vbBCgJCpJybIxQiZY2zg/dWtJkgmznV2mk5wkTVgWDyCBRCaMpEAlglRJtKmxUmPSAoSLhRBSUK5qHpzcp2kaqqpmOpljLZycnrFaXFDkOeDSgOzv75OPpF+TmKnxe7ElCmH8gfWwrddWrF70GTd662M8EmlViBas9uFInksPrr5DaImIR9rC74WQKkPIjnDbsM5CECL/W6nPL0ssPcdIQQeOcrAXncdRSyMQVmClwGjHZFljsMa4nEAXC87PL1gt1yQq5fq1m1y/fhORWHZ2Z9w/fZakkpxeVCTJiEZ71W3lemi1RTcahEQlAlM7wn7//n1SOUWJEdOJJFUZbdqTliuPiF40lO4UupxJ1sc2WAuN1o5ZlKLNTGP9WlgZPO4Ge37AkG2L04l/H0r17T0mMGtOagwMqbXCx3L4lBve82ishCcGaZu7SvtUQZPJhKqqKMuS5cGKYrVmvS5YrVatd1NIveGY2LDVRC8s4IXgRRMFF2Xap5rxxMSfwTC2bcJ690WUN1wfdnxIeWOk+ELXNiBGyJ5ChIO3Tf9rrW3dw2L3t+6dBKyLtVH0pem3MUxhMYRNQ1jnR7/t3vgz7lPMpcTxGa1NQYg2UrIlmtH7OmmDzXcEVdngegw9pGTjg+t+y9KMnZ19ZuM5i9UJwgpUkjDOZ+zvHiKFZrE8R1c1k9GU+Wyfsqw5PT0F0yCtoG5Kau2ipaWBJM+wWrMoVtx/cI/zixWj0QRroKorslShbUF1d42QoBLFrtwjFbLjgD3NNtaQRHt22xx3P9JjJlrmPPo9uA8O1TLtXh2oAR0fFRinlkvxQqkA6RIExgxEUK4GIhAYn/Ccbrp3h7e4x01P7RICy9rvOAJprG3ToRhPEIyP5D0+Oeb8/AyARx99lC958klGo5xVueT6/Brjec6y3Kf+bMlifUyajBFCU5e6DXQzxiIT1/E0TVksS5YX90DnJHKCUhkqy71w6wJL6TGg/fVxDBK4GHCXwC+sgRSOwFlrW0mxJa5KIfRgDenjuvhzWL1vuD96c2vcqYvnt/uULbMW8IwQDikHZk/rzhaRZRlVVbfZImazGabRPjjUeTCdn5+zWCxab6aWQADWXl51MIYXTRR6EZHRwIdIXna+phuT8GJEmCFXHMM2/eDwPUNuukcMeqw6SOvctFrPneHZj8cUtRH6kSjHheigamDo0moHbW0f87Z5iRH8MP1CTEi6a9vnOd7Mxnpk4L1nhr/H0I655YQ3PZMug5ZJtoEAC5TKODy4xp3bD/G5p2tWxQWYhFRmlMuS5fKcui4QJIxGU3Z29mgaQ1U2LJbngEImOaDRjSZJMjKZI7FYDYuLNavygqJaYYwlSRLy8RgLnC8ukPcgzTJkkjBFkqYhi6xFJcrlKTKxx9wLEHEsNngnhEkKSqKeSiiS2Hxyh7j94ToFCS1IykKI1i6wjeOPJzyWeKzZtjZBMur61W9i86y258k6xGmAxriI3bPTM4qi5PatW7zmNa9hb2/P6bg1rFclo3xKPklZlSs+/qkF1mhUmlJVa9brkqqqUalEaYO1mkTkaAPLZcGpPGV/94K93QNnw3BOudu6PUDYw/GYniQeq3nitXSu72Lr+RyeuTYGILr5MiatXS/R2YLie0Jwas/+JATYOCaow0Eh8tnxCN4eobTb655IzOfzlkCEiOrg+lqW5ZZ9sQkvKUvqcODx4GOIGafh4XohQvF5uf0XARuEBDYW23FjXiWmlCMKxm74fvfa2UCewbDuYxaioLBgOI659643m20ORc/4c7iR42fj8XTceXsDiCB5eVVElM5cm5AJNZ4YPLfZ9cG0Cr0tc8gmAeqPMdaJCyaTOQ899AjWWp559nMU1YrlWUmxrqnrtTN8SydS68aSZxP29g4RUlJUKzIFaaooipJROkJayVqsgZw0U1xcLFku11gL+XSMTDUSjSlLTs+PUHdzVJpyzQrms12yzEWhCscadww6m8bNwDT0JTix8RkIaO+7tSiVtJdNmHvbMVs2rNlgjYfqQWBDch321RjbqlKG6+A+trtjD/d46HvMyVprKMuSoiw5PDzksccfZzafU1UVUinnaioteTai0EtuXnsZJyfHPDi+R2MajFaUhcFYUKnwEbsaaxvA5SwqapfGumk0WOs9/dy0dohc0KUeCSJDt3aBQHe/Oay0jZm0dmDviWduQIxjIhSeje/dymBdwmA491h6+ZvCvgjtSe9iGjOYIfgw7CNrglShyfKM8XjM/v6+j6Z29ocQMPdi4CXXaN7GtcTcdFi0HkKAy++1TsUiZSduDbnz+N3b2tuGNLv7u00RRLX4PQIQ1iHJkJgvjlDcxmkEaHTj39GluA2cnog4j/CclJdtjk2C0M6i6vTIja9pIIRw1/27GqNR3hDn5k54T16HpIPILIXjhrXx4nT0/t5m9ngtEINYHfX51qNbF3rjNcZxfJPxnEcffZzpdIejo/usixWr9QIhUuazKatixeqiZH9XIUXKznwPlaUsl2eIxJKmiklZIYUgVQlZplicXZCPJCrViLRBCJjNU7LEcf65kRTriqOTeyAVurHI2wql5iQBWWDAOwkErrQlhmJDO/TCMCAe7XrGbqb080kxQC7DvFdu//RziLXrZaPzZmUUnR4tZtx/u8ngbWM+wnsCAjJau7w/RiMSyZ2HXsbO7o7PRZXRNA1SJUjlJkyYjFTCy26/kqrUnJ0/AJFhtHJqO+MN/VJhjWU0GlPmTgVbNY1TfTSaRCmcUkuhCY4fPtBNSoz1e826lXTZBiTSDler76IbcExj9MYcxTC0JQwR//AcDHGjILIBevVqS2TpSwrbzmRY+7DGSlmgszd028CSNTlN07TrttPs+qyuK5ec70XASyIKYoAYwt/D6MiWmg+uBVF4yCE7ndd2jjl+vhO7uk09NHwP+xZUQxttm0htRHRwbZdZUUrZpgsYcgZSSozWKOlEa+0jKpXyQVTtgfYLa4z3te+34biAQHg2A/nisXw+aaE3/5ZWlSV8gxbbRi0HX/Vt7Q03pHVUqfee4Zy0e0E4JRre+yaEmUjhDoBFMB7NuHM752D/GhcXZ9y9+ywXy1NMI0hkTl0ayqJmlAuSJCPRNZPJDJlYynqJkMGdVpONJDtyjK4bZNIg0hFplpHnGdZo6qomHyc0uqEqCo6P7yOQpGlCkghmswnKmNbFOIyrWyMvaXmi2xrhB/M9ZGSGXLhuTCs9hoBPt+e9t4iwziDtmw4FltpI9OjsbCJwV5go4H/ZepxAOFebEl0fhmck9D2c26ZpWg5Va83OfIf5zg61blCNJPGeNCpJHQPTGAQpWMtscsDB3i0WyyW6cefA2eskSoGuNeNszs7BDQ72Ep5/5ojzswUXOwvG+YT0YA8hXPDaMIgvxCj4Dgc+CGMNSnS6+aGt0kbrKKUEvWXfR/N8qeqOzbO3QVgRbf6qkIbC5WOi84IknFvnRBG8GQNzYT2BkyFjA0FiMJHa06WX0TqkOElomoQkUaRpynw+29r/IbyE1Nl2A/lvkxjCRAvRR2aXiVvDzxfavNtyz4frw03dImWfJ0lGXFdLkGiZrI2CNjF3EN4bH0x3zSP2Fhk4wiAC5wat1BC40Hhsdkufur73JY3hPA4JxEYa62iDOw6yUyfZwb1DBDb8jI3ll61V2z/oh9P7+XWPCZ9XRpCojMkkYZSOEAaSI8VifUZVNNRNzfnJBYnKme/MGecjBJpVcU6jK8pi5YiPtRjdkKUJo0lKY1JkCvlo5HzeqwakIR2lTFBATVUuOT656wKpUomS1xCTMSpJXCoKsTm3HRcZf4re7wHJe3Z1Y247IjJU9XREqPLVvRCgUkU+yhF4199I+u5cmMPadoWuuuJv1n8J3Kd/v/BMT2+B+pJNuOokxO43ATRNg1KKRx59lGuHh05X7VVHssf0KZRIsBqEaRhlUxI5YlUvSFRGkgiUdC7sViikTLh962W87NZj/PL/+TE+88nf4JlnngMLWZqwuzcnqMYCdMW+hkbcQNAFxvTnexjoZ4z2LqmiTc0T2tn2d3j2xRCI9hrdOZTQevKFc9lrX+C8vrxKGjpbibtPDr67Mcb7KGbcnC1CtvmYXgy8pDiFzzf4rqObInaMRLZR1m1ccJy3PUagcTTmZYRp+P2yBfb8WneoRSfaxc9u2whDQiIiDk16jjCs4DbV0TZ9ZHx9yHUMCWZvzgYbBRFJBu6HqGhPlC4gev82kFL6Qjdb3rn1WYGrZRBmtk1k7Q5AuCf8ZgR5PmY22/GBbM7OU5U1F+cLVKKYzcdMRmPqesliUSKA8WiEwLJeLSnWa3TaoKSkxuWMmUwmLnMlS3RjSXPBhBSlDOVqyfHxXRIFiQTBdfLRCJVKZNIFFcX7K07j0e2tTqKUop8iIoZOBx4TWve/0FZd11xcXHB+fo7BMNuZsaf2yfMcQnI6+lJMjGwY/OuSurUKiW6Fenm/+j8HG1JQXyohMdq00m7TNOR5zs7uDmmWsVy6WgkyTZjmmYv2F8KL4QLTQF021KWmKl2BJZlYklGCkIamrpAiI0lz9g+us7O7x+te9wYUOZ/5xCc5Pz1ntbtDlkry6RQbSbjGaGer2dib3RzHEtw2hjOwbpcxYEPV0fD5FwOBTFmHGHo2oUCgW4SOREp8bI7PHUZfZeg+u/a3aWqE6OpmBG3HbzlR6Ou6BveJzUmLEWtf2oCYe4oR4xChx2J6vGBxH+IJDwhae88PIUQbWbpN5QQdJxJvpOEGAcdxSxxF143u91UElZZoC2f45jf6OZyb9nt7e8zn+RKVLdL3z3eq7z5hiZFQbzHo9Oa282uP0Yjw10I2yHaGXoAQh2eHdaG72PY+CARJmjKb7aJSRZoqVuul8wZSqfOSMhZTN6hUMpu6yl7FeoVuGjAGJRKHNBsXIJdKxXy2y8sffojl6oKnn/0NlsslEkuuUtLEKbGqasn9+885zk3DfGeX0XxKPhq1B1h4aQTb9wzaGMeAmMej7ySF/rXAfQe7QqgH/ODoiLIuuaYPGXvCFlQf28/PEBmoVtXQqpBsSMvQ2e8uk1RfSGoMrqshQ7JUkiRLSXTj1CI+uEwp2eayMo2hqQyLixXL5QrdaKxoUFa6usQY6sqSZznz+Q4g2T+4xqOveAWf+8xnOT46Ik9cqmqRZSRZ2utnDI5whetBq9DHI0O1kBAuK7OMONl4PeP9ftm8b6z18Ppg3Xu2pAhaZG+HGg3VW68wpsDEOlVt7KzgnwuZdqVEWNnLGPRC8JKC17Zx+i8kMfQHuykRxBMd2t6W4yfuA3Ri0ZDTjt03W+5J0tZ2dVTZdLldIrhs0dvnbOef3Abx4YPdML3rXVGs/vhCoriWW3c/dESgJXqhHGO0yYbI3fZ/kwMONRg2Q71rC+2mcIQgIjzhejwP4YwMRO5tiGkTIlG4/doRn/g+KSVauoAyqRIsUJYVk0nWRkAbUzEep8hEkCVjGmVpqhXCSkZZhsgkRVGgDcymU25efxl7OzdZLNc0TSdCYwWVqRiNJaumZrk849naUq41O7t7HNy6zv7BAdPpdMMVcRPxR8jSmN53N+ED1acd3BOtbezMUFcVi+WCfJy3OfeH6pEhsxTv+yCX9ZBZr6/xtukj/tBWL3WE6a91yGhQ13XrCtnGMEmwtsGivFHd0NQNq8WKi9ML6qJGJQqV4mwJRiOEIk1S5vMdcp9001rL8fExFxcXpEJwfHTEbDZBZBnzvV2fmdTZ8qy397QzKgLyDfu1n0Z/iJCtJ/hKdKhwiEu2MUIvBQLzHNayxRNm8z5LqJkdzbvPAdVnwtxguzPl9pyFLtNskiCieJiQzv3zwRfkfRR068M8PO0BikTlrRSVTeIStz/kyrctRCziX4aohkapuP1hv90nG5sgbIyhPcNa7z0taFUHXb58NVjAUONBdQjExiKu7CqMhfmJagHEc3Upl4hB+JThWEvwXzKRx1JA+tYGWaAP2xBg/G/be7dBT3GxYWDo7gjNrFYrjk+OWSzPqCrnIbFel35Oa/JRSjMbo1JB2ZRIIcjTCUoK0iTxNCdFJSmj0RQpEo6Pznj+7n1OT09JEsFkPHZitFY0VYNKgcqyWrs0DcvVkhpnAEyThDTLnKuf0J0tCaL59EyaDUGQW5ieMGLP/pmeZO0OtvF64zzPmc6mjMdjLlZLx1HbLnApRmxhbfrEQBC84MI97pmuNjPW531rVYqbkgZ4wuBVjHqw70MxmcZXGVNKtRXIpBRY01A3DUZbjBaslmse3L/PxcU5WtcgG0ZJSqIsk/GYUTbm5GhBlmW+v1BWJUII9vf3OT8+pqorHhwfkc5m5LMJQjmkmibKq0jcZgraAGut57ZdjWQRkK3dkkXWUxNjbVu/OZYqevd/HnrQSpJBSg+Ml+nqtmijIwaZjTMurHCBlnTpcgKx2Dx3HkeI7ZqTmImOv38+ePFEQVjvM2ux3qDabioTdVo410Nr8DV2O4+NdtKEwPpausL6hHGRDz3WtknktNZtGLgxPpGaFNRGtxb6YAglHI6QEkNK0Nq1jXAeR16dFDbgkPMyXtUUErq1glkby+X6bqxpD1r43QYRWtByM66oh+gMvOHQWjdPLlrYRByCccW4pStwE0dlhvYCEuhLRTLakO53KQJi70tCIogBQyTm1y9wXtpLR43WbbbIgBji7+3z7kU0IUOude/qaiV3WTkDNLpBKsnOzg6TyYiqdsnv0jT1rnRL6rri4rxCmxqVuOSMSZqAkiiZMBpnzHb2XRoFo2koWZ4fsV6fUldrpEiQcgJCkOQ5srYkVjDLUi5OlyyrI/RqhbonGKmUjITdvV1UmmOxNBKEgkbXIAyjPPdpkRVKSJfTJjonQaIUuKpzTV2jm4o0TdsAulBJTKkEK6GpG5J8xOGNG4g0wWLROqRpcAgfYdvyr60o5/P8yKgOb1gS55USvJjcWinnl0xgI6xjLVvEI2XiPOWsz5Hlj5S1FpRiMplwenqKtZbJZEKSJD0PICskIlEu8rkuOVuc8/yDey5OJLNo6yKjaSS6gGw0RVdLTu9fQA1NU5ImE576ki/hYGfOL/zLn0Pr2kWvC6fqk0IhrMA0GiWdzUPIyCVbCIwQWOuYJKf28qrIxrlpuR1q/d+e4HskHZxSAjPgD5bf5ARzSZsRoU3ZjQCfhVkifS1tl15Dh+zEPYnP+NioGNG6Z4IKqAPjVszjysBkuF3mVRNGt/hFCPeE9PshaCheDLwEm8L2RsNmiq70Re92AjxiE6INM3dZGG1XijriRreK7K247P3uB9JCq6qwDsHZS7yVtrcZEuSFRtxYHOXrG7E62tVx0LGdY6NfYqhlb5sacP6dUdYRVosQDvn0ke9wI+EOtv/RROUGlVQR8g9uip0KwwapLozLBtdcR4CCeM0ggGqbbjYMwtO8aJCBWbY9IheQ/0xOqfPcV6GqkVIymbji7U2jqeuS9bpgubzgfHFMVZaudm0CTVMgpeMqy7qkWBRoU2Nszc58jGQXbS1pkoFInPdMkjGdj51EoDLOz86py4KL81MwEt1oqqpiPtthPB4jlaIua1bF0jFFc8sod2VoO4nSdgRVCB8c6Moproo1q/MzRnnKZDpnPJt71YfwMSMS6T1E9vb2GE/GlFVJmma4DJrWc5lxoZ7orPh9GnOEw4A3t/bWM7G2PbcW0VbaCypRi3QeMCHpnOfEg3TSNA2j0WiLWrFLkaGtK8VZ1iVVvcZajVSQpjmJUBhtkWLM6dGKcmV5bnWfo3vHXD+8DRayLOWhhx+iqgtOTo8Zj0bMdvYYj8ckKukxRNsi/U1gRv0eFKJzww57OpyjkMXWeg7fnYkofxVhP28RFXqSoWuw56XoGdyAm3q4kNjWE+GSwVjC2gwlF2lxqccFbUZWp60wrW10qLF4MfCSDc3DxuMBxosUOhQo6UvVxQ3bbdv0/x+KeS80+MtULgHi5xOVDq65A7eduPSJWPcOR7jag2nxEsSwjUFfpD+gNmSrDG27xOldH/pj6aSkjiPokTXvgx8kBre5wnfTEgTps4a2bn9SIoFUdjW2g/pwgxhE/XFz4biZII0FpCSisp/xvda6KGZrBFIlgJMC0rFkMpmxs2NpmpLV+oDnn3+e5eqCsqrQusIYzWiUY6VTRYErEzkdzZjmOeuiJBuNUNmYi4s15boCElKVkB6MmM/3qCtNtbScn55RPlOxLtfs7u6xu7tLluUUdcl6vXKp4g2IHYnNRKtPD+vZrbml0c6AfHZ2xvHzd8nShL2DigMhyUfj1u/eISLbqmBGoxwhu9ol4bBvIvpoJ0b7b2hb66mcAAVR5bK+WlCEDRZzKHTnMQRMZVnWY4JC+xbR5v+yGBpd0zQVUjkvGJU6xDmbznjsla/m9GTBfN6wuCi49+CI2Wyf+TSnqhuyNOWRVzzG/vkhdVWTpGkvB9tQrTuci1YmDUxPtEfDnhdO/YHFtFNhQz3kvraz/e7O28bp7fWrhw+i68O12sSLm8wwXK76Ud6hJbZLtVHyLxHnBvhNcUkd/ha7PsW+y8H9KuZogiokdD82crWZIWMCsIVabzOCb7MxhN97hySyXQjZt2Vsg/57vFhCn3Numr5B2vikVjIKoGn3Wxhrj/gM5/aFiRpsiVMY3D/cIPHY27XZQtytdemmh4b9S20+FqwVbcT0UJpoi6Z7rsmajiBUlYtGdj7sqVOdRPrYLBuRpIrZbIeiWPHc889ydHTfJ867QNuGi+UZQlh2dmbMJjOkNQizZGe+z+G1W6yKmqKoqcrKV7NyBaR0o7n//BGnF2fcP3mOZblkcjJlNp17H28nok/HY+qiwmjDbD73Ek1Uz9uY1qBndMNyccHRg/sc3XseKWC9LjDaMt/dYzyZIGXdEVwJRgdGpA8tg7UF0QtemBHa3BTuf5KAOL0d0NgoEl60YxJhrfw6Bu+jzT3m2jVYn3xO05gSlCFLFUmaUDcVo3TCy+48zmOPfSnGKsbZmOPjE46Ojlisl6R5Tp7mVE2NSlJm833ndqzr9p1Dp5Pe8KTLaWWtS13juJ9uzwfGxuVnDpLwFkQ9WIZORRYSrW66q8ZMasvZx8cjQtZDHDXEKcP5jSHWUNhB+0NcEK69WCLxkg3NQ4Pjtu/9Dg/VDCCGSG4wqHjTb3IBXGpAjvvXEZGo+U7DgdaOGw9qvrAgwbd3+G/bOF3swaa7mtNtRrYA337IYR/Uani7CsLnNrH9AMHhe6PR9g7ptj6Gudi2SbbN7TZxtSUOWwjLUE3W75tsdbwh1LYvLdpWRWWcZqpVHaVpAli0blCqc+213kahpCt3OJlMeeSRVzCbzXjw4HnqukKmgjTJWSzPESyZZjkjlYBWTEY7HOzfYKdxmSeLsmK1XlNXGiHh9OwULWuyCcjSUNRnlGcLFstT904rydKMpppRFGuEEp0XjHSqnyRNW+5Va01ZFpydnXJ+foZBUxYl9f2GsqrY3dtn7+CQnfkO+ShHKklVVRitSdOMJElIfXuhGPzWnSAcZy7ldsl6eK8TYvpcbFiTIOFYr561Xm0qojbTNGU2m7VFt0K7bo+Jdm9CQ90U1E2BofYEwiKU4uatOzz2+KvZ3b/u0mrXFWqxYDTOkSmsiwVpKsG6AlQqUYwnE9YrF83ucTwBhwYPJK8M9aOhHc8Q0fbwQcT9DyXfri5LdGvAIVIgTB83QR8pX3Z+X4iAW2sJpSBbqdpGv3VPRdf6OKBna/RwmRvsNnhJROGFKRy972FTBj1o52tNL622o+b977Er2CaiM1jTnwStdStqD6ObLRFRwtkzLF3eItmKjwBdP+P+xOONFzoY/YQQnVHa2taoFxAZiE7fR7cpY4akP7edyiWe9wDDQLgwB9uI2WVpfofc5pDoxdft4J5t7sBdez6KVAqkFc5s3zrcdEZyawza6layqmtnC0gQIIy3C0RcVsvpCZIkxVhNlibcuHGLyWTManVBg2a1vPDZOgWrZYlNXLqFyWhOpkbU5dobKxMSlaFV5QLHlgu0Ldg5HKNyw+J8xWpZ0JQFiUpdMJidUKSCqlyT5QnaaMajCVIpZrMZs9mMLM995ayK5XLB2dkx6/WC+WzEuSk5X5xwdnHGyekRBxdnXLt2g/nuLqM8p6ycMTrLc6zP9DrMThz2Ym+/sF1VsI2ZcYoSGyHLeA1p8zO10vzgnAfiF86N6O2NoKSxGK2pqpKyXIHQjMYZCEM2nnDrzh129w9RaYrFUNQFja0omzWNESwbw/n5KVjJ9es3mU12SIR05Vt92y2O8PsxYO0Q5BfOmeg631MfaR+93o5dyY34GnozFBA2nhptzm/MAAe7Ql3XKCEvPXfDz6CCizUEQbU4XNuAI3rr5dWNl5/Pzw8vSX10GTcekE+rCol9q4eTBq3vc/tso3sJ6IQQG5xI97crUhHyhwRRcIgY24naEqgTT2BspAoc3pBbHx7CcD1UJAO8K7FvVym030BBEtJak0ZBSO5GgbaOu+nltmm9d7rDdllenaF6Jr4Wb5DLNmK8vsPN2f42eO82N91h200TG/b8fnD+kK5NKZHWUpuGqi7RpibLFM4eUCNEgjYFxrrgIvc+jZSZk/IEzktGKPb2Dtnb22NdFZyojKJoWC4vKEtDuVyzM9uhLDXHRydUVY0VCgtUde3c/1LJZJJzfLFAZg2HN3fYP9zh3vMPWJ6XmNqQJgl7BzOsEVRFxfHxA46PT0iTnCTLuHHzJtevG0ajEXVTu0R/qyWLpeOAb77sGrO9Eb/xmc9ydrokKSXHR84DrapK8nzEel2wu7/Xlg7dtl7bpTvbO3/xmYz1/W69vNdbhMBCaUgT7TcQrfOCMbZ12pBeNdZEbQc1r3O5hqbWaGsom5JSlyA1o3FO2TSMJylJKliuz9k9mCMTxfHFgvvHz/KpT32cuqypy4bxaMLOdI+9vT0aXboKfHREcTNItT8/7Xmw4Sz196hUsvUgsv4Mmvambs8bIRDW6e5DIGow+g9xw1AzEq4NRbyYsG8yfB1xGuZ428a8hXXexriGM7ot99MLwYsmCsPi2MNNG3c0LtkZF/DYtnAAMlGdW5dfDeeFJwjeMWGVrP9fPPDhJMXfh/xT6EPrbkYsDUiUjyUgSBXGtkmr2vbbd/THM+Sgu464R+JFDV4avbn0/1wsQ0cUXFK2GCGIjQ1g48mL2xv0J6iwhNxUL8hovZToXIJtJLltzG+EtNzeiAlQXAbGif2OULiAOiMVxpQoZbG2xiJJVIqQxqW88NWwtM/rY6zF6qaVRBCWJFEIHELLsjHXrt0kzyecn52yurjg9OgBi1XBYrXG+ALqWZ6RpAlSW6x0uXXycYpKBY01JJlksjMhyxOW5yuaErJkwny2izWSVOU8uPeAu8/dQ8qEJMnAQlmWTs1jDU1Tsy6WKAnj6QSRaEbzhL3rOxhhSERCkkgwGqMbFhfnnJyeMx6PnZSQJi0i31Z4Kp7/bfxfzCjE6yOEpfFJ7az1ZWyt071LIdFt7YLA1HUS6zZOs89UdV5rRjeUpfM6Go0z6sZ7Ick5Z6tjHp4+ihoZzs+OOb+4x8XiAWW1pKk149GYhx96iOuHN5mMR5TFGiUTZ9cZzMFlEPZ+kA56CNUzqw6XeCQuxNZ5dPczyInVx2lD/BYgViNt4prNbNEbrx2McZtUEtqO1b3DM/pi4xMCvKSEeDGXPdx02zjV+PdeHiPRtekfbnWAAVnFSM5G7xu+67LN0Vuo9l4I1CUYQk000W4D+cAkz+EHEW04VtdMqGW2Seh6C+O5rlieDW207xpwHVob768M1jo9fTf2F1inwVy1fYj61bnqbT7bPhfWZKvdYPBcJEFIKZEKjI5dV6LD6G0OjsFwHG6SJv6g+iRmWqObhiTJkFLRZXQVGC2cpGAhSRSh3KI1TmmgZMbB/nX2dg5o6pKTwwcURUmSjHx8gCNQQhiKYolQMN2Z0uiSPM9xZRxdvyaTManKQEvSZIoUKdPxDjvzPR7cP6JpaoRwiffu37vHyekpSim0aUhSSZ4n3Lp9gySHBxf3SDJBOlXsmCnSpgibAJq6KiiKuiUqiWeqts3z8BwM1yC+FiPscI+UQZWpEf4ehZfow33EZ2K7qniI5ILEQuC5raFuaoxxZTHX1QoSzbo+52xxD5EW/MbTv8azzz5DVVZcLI65dm2fvZ1DDnavM5/sgZE0TeNqnSQup9WQUw77dutcxXuOPk5qj2IgBh5P+EETGg74I7iWhqmKxx7Pf+hXX429vX/b1pFuGdoVCD93RCR0dTvO2SZRfL4zHMNLKscZb4DYLz+8dCNaMOJIWyQuZSumbRW12L7ZhxAvxmVSi9sYzhNiKDWE0nwMKPzQxS5Q2iGVxjou87J+9ghWkGy8HjNwKOHndg79fGrjc9e39RoExmxPEd6Nc/v8bCNWn4+Qxi6Qaqjy2gK99Qo9kWH9OzEvrIHx0T/u0CqkSFDSRcoWRe3zwSeMRglZmjvJU7mkc9q7eSKCO6gPcJTJIEcQqCTn8PotVwAGR5tOz464uDij1iUqFRwc7nJ2XnJ2dowQivlsDykU5XqNSEAbqCuNNg3TyQQrFGcXS4qq9pKUiyqtqopiXZHkzsNGSsve/hxjGlbLgrVckCrh3HxHQKVdqvA8YblaslqWCOuCLI2xmKbZjjSidWpVBLZT78RM2PC5NhLbMyFBXWpxAWFSqh5nbXTj9OFRQrbw/mC/GyaMs8JgrKaqS5q6pqpryrpAi5LRKEElDSdnz/KrH/sFiqJgsViiK9BacufWy9mbXydTU4yWCKsQIa1G5SO8430XzccLw+B8tOc++iWa63CeLLZXodD6OYjTXW+ToIdrFtOb8FvAnfG9LY4KxKgdYf+zR0OimKThvngh3PhC8AVVXos7sK1GwhBi8WZ4fSh2xd9j5LytvfBbrG/rtSeER77bvXN674RIMtjs3wYnJlxVJHqLH/oAxLy4P4ixyVeIjnsJ7qqdUa97V/yvG2d/Y8Rvi8e5TXLrjeGSuRjec9n9G3lyvO7WHV0fZSttlLHT9dTabk0FCilTsmxMXVVcLC4oy4IsG2GtRKm89UJy42mAjNbjxDq1h5TKR2Abnyp4RFUXIC3ZaAxG01SV4xCV4PzklMZUVHqFFYbzxTmNMezt7SHTjLpaIZBo7aKOkySjKBugJs8mPProYzx8+2UslitAslquefpzz1IWJePpiHzk0j+UZYFIGhpbIRAuglkIVOLqVl87OOD8ZEWWjkhUjpKKRjfUZb2hXhCyK7i0lQGKJLamaYAuqKlvk/IRzVqjmwq09cFzPto6EFd88BWb79p21o0xCOUS4jVN4/vg9muaJ86jSDSsyzUf/+T/RZaNUDKjWDYc7t9mPJ4iSJBkCFKUSDC1zxAgJPaS1C+fT4p1QkyIzo5cpaEzNtPf5+FcJrJT3Q21Bdtw13B+rLW9qozhvm2q5u7c9vFeTB623b853s2zHuPMzwcvXn3EZorWIH6F38O12I4gvHsmEYccq1BaKSOkaSCmosOp8KKU54zCewNX1A5axPfSptLonFC7EPVWbRWeCxKE9TYCopQWoRcyuj8IA7alQV78Gyw4gzaE88pxpKFLYuVcCyVIH+sthFehxHO/hci1xCMau/DjFZ3UhP8ekEtvXv2M2LCsYvC9m9zen7FEZIV3Q40PUDQb7Vz7RpVSJCohzzIvHbmss112WdHOaVvkRim3B2xQcQnnZi6kI0RCYoyr42ytwugGEChh2d+7xt7OHtPJjHv3nkUYwWSas1oUbh+aEaYS6DpFpWOmecqtW3e4fu0G5xcrxqMp89kM6XPqrFYFAsmDo2MMLpL39q2bjMYJ9x/c4/jkmCS3qN3gSmkx1kk2+TinQXN07iJ5H33kMQSS1WqNS2lhe1l2VZJ0jE+UMkUkEitdWorGONVbVboo8CxNmYxd9LZMUoxye1FqjagL9HqFrmuMhUKlZJM52WRKko1cYJRHak666PZ6OMetWs8E47/GlexcUxRrX5wHRllOmmrW6zVJKrHaUK4qEikxhSAxY6rzBpUZ909aVHA/lT6MQCq64McOYl99EfeRoIkIOYJEm7HAcf7+CEvZ5YPyD1vrYzeEP6MRohdeyojf10pgRM4hQrq0Hp4whoMgNs6NjRgq8AEU0VELNg/RnlALrowsQZPQuch3+bmCBBFw7W8yURhy7a1qRYjIa4G2gITBJ98SwnN1l4tXCie6GmIqZ9FtQEmkB7R+Xf0kB6rfimREnLRHQMK6kHCBwIgOQSGEr9DkXNKMNv7A+hqoobymsC1C9wop10dLmwogHA4htlFwhwhcgfCO7od+hmR41vjyesa0Uss2FcCw+fbACuuRcffeVpKiW6N2Y0dpDGJCrAIXE7gp/+nsAJticSvUh03fcQD+YHmJDZefH4KU4d6rJCRKkCaSnfmM8Sgjy1Lff03TOPdMi6WxYV/Jtm9hrGFitG4iqRGUcP7+lbXY2pAkEzJZMM33sLLBlBZTKg72nU5byoQbew8zm81J0wylUkbpmMn1ucMrQqCxlEXNeDJhNJqQjkY8enxElmfM51PAcO/B86zXBbMsByOp1zVWSYwW2ETSCMu9o/s0VpOOnfSjrUEqyXjiEvi5qG1n7M/z3AXxpSlNo301P4ltwEjvi64rivWK87NTjh8cMR6NuHXtBpN8hMgyl7KjqdDVknp5xvr8mGK1pNEWkU/Zv/UwaT7yUclB5TuoPBifIbwKAyfJGB+fUdeVUyE1JSq1zv5SWRKpMLXForCNoG4MTak4ai4Qi3NmY8lsKpjP55jcu5TjCvhoownm4L5E7GNi6Lhsjxm8h6DvezhPniCEzR8YpFZVZKJAweh9nbZEeELh6x2I8IqgwgvzJLwoIqPCOgNJzzOgQnhmTQiM1a2auWWi/N4I59b69yqhQIfEhyH7qvVEIrYHSrbLG5vwBQevDUW3nqpDbiKz4X3x9bbjon/v8PlAGAKlbClr9MxQuy5agmLbSQ7udwCS4G/tiYrejPx7IXXLtvuG7nJt/+IUD6IjZFIKX1bPtGkEuo3Wtd2JnI6w9FVlm/N1mU5xKP5vE4nj57cR9EBsNqErjhTfv424BYklBEUJIRhPXK6h9XpNURRteuae+690nJHWTVsGVQintghiv7vW1dw2jUE3hqpsKIuK9apCkHDv3hFHx/dI0oQnn3wd167fQKCYTudk2QghJGVRsVheEGoRVE1FUzdkacZkOmUymTLf2eHevXucnh47//yqYHd3l9l8QpoJTotnWVcabcBoycgbsXdvXGfv8UNSlTu3zgaklwhWqxWr5ZLlconWmt2dHXZ3dinXBWVR0FQ11lqSTCETgRAGaxvW6wWnJ0esz89Ynmjqi/tc2zv0ajbnTtmUS4rTB1Src6qioEYwO7jBtVt3SCU4std55PT2Mf3zP/zn7D41Ta1dzE5YCyFIUoU2lqrUJCRMxnMqK1lcrGlWD5iOKnZ3S5qmYbYzJ80zlBAdQh/0J1athu8h/0/rCi3EhsPEsK1w/ofnYZve353NwJSAM6xvw1nbYwsELntxe94ECKta/CACQegedB+hHz4U3RGI6Dz5sbT5nYYalBcJLylOYair2nbQwyCHz4W/4w4GBKiE8mqBfmHyOAFY205LGPoQuFl3M9Gk9H8TdrgZorHBhqtmPOZtc7LRh0F/48MkZbfUAem7/hnwyi1jdKuL7fTBfcPUpX2IiOrl+srLDc1DArKRPiB6bniAOpBBJR2pvLbbNoKoMQy4kUq1xcaldPV/iZgNrbWPgG7aOQnpJeLfpJAkSeqqWDWOy6zLktPTE05OTqibgtPzU1arkvFUslyWzOc1aSrJsjFZmpGmOUYvsWaBaA3wIKUiz0ctMcvznOvXr3Pv3vMAXL9+nUcffRhrDf/yF3+O1aKhtjCdOMM5WpGpEXduv5xM5RTr2iHTsnLZUyvJ2dkZJycnXJyfA3A23+Hw4JCyKLi4OKcsSqw17O1MyVJJ1awxpqBuVmBr7ty8zsXJKfee/hjLB1MSlSJRgEDqCr1egK4AULlTlY2yBCmcS7ASAnyW1iEyHZ7rligY5zlXlK5MpxCKROEJlotfSdIcWdcoOeLw4BbqYML5cUGx0pxfXLAuClbrFQfFAXsH+0ync9+Xy7UNm5qMwCioltnatvc729bmuRl+32CO6DQV4f5uXoQnSv3EncIf+uDlGNoR7gUEzPZi2NCg5g6MVTwXMVEIffpNj2h+odw6G5NtO658GzIKn0olSGkjj0Vx6bs6rhXvhrjpYjacyY6QdX8ba5wuLiZy0ftldP3zTWI84XE/t3lR+QuXLrZKJKr1CY/SL4ttCHWLm9uAEA//3kbUh/MU+j/Un1429nD/8LpsjXMdQYih2xMdNxWIQqN1m6u/abwkEP4zTrda1zWVV6vEAVRlWVJVFevViuVqhdEGGRCahSzNkBZWqzVFUSKkoKoMVWW5decaRdHw4MEJu7v7CJ+kz2VnXbJYLJlMJiglqEqnJwfnAeXyHyn29/e5ceMGVVWyu7fHfD5DSsHjjz3Bz/z/nnVIsk7J8zFGwiidYBrLuizAutoDZVGBhaqpuHfvHifHx1hjmU6nnJ6ecn5+TlNWlKXjppUULM9PSZWlbpZYs8LYgus3drl57RFuHYyRzYLnn3keRcI4d+9MrAZdgm5QWcZkNGK+M2cyzlGJRAsI8nXYc8M1HBIKY1xRnaIoXI4na8nynCw3IF3a8brWWANKjplND7h+7Q4JY6Z5xfK84p6+z2K5oDoqqJqyTWE/FXNE0vfF37YHu8DUvnNG6OcLSf2BeRxKR717nOGMIO0H5BF7e3V2lsAYOSNB66ZK0O9HmoP2r44wXKYx6ROqSBsyIGDx2LcxipfBS4pTCAO+7JC3LxeCUDS8f4+I/nWiGNr0fOdjr5YXQmZt26KP9II+zitZ2glyUcam5Sr7Ojt8ec1NtcllQWnbglOGBKHlYAJJDyCCbraTUKSUqFSRaeeeafQmd0ZozW7hXLYQg+HzL/QMbB72bYRvCJsqBbH1eSmdy2UoNCJlMBgDUrhSi42g0ZrpdIpKEtI8a+fRqYEadF1jtXa2oGgcjfEGRWtpqorFYslqufapti1KKkZp5g+/ZTbd4eUPTVFKcedldxiNx6Rpwt7uAUIoqqqmrmuEEOzs7JAkCXVdMZlMmU4njEe5S60tBWW55vz8lDRNmM+nlEVJWa5ZLpesVxW3r7+cqik5OT5GZTnjbEaWjDk5OqGpNVjB537jaa9GtNRNg26ciuratWvs7e3x3HPP8eyzz5IkCfP5nJ08RwLrs1NWFwuK8gyV1qQZXL++x3Sakqcpj77iDudHpywvChIMopE0RpMIS9MYsnHC/sEhu/uHpKMpNklRKsXSqSe2rXN8vWkcMVhdLDg+OmJxcYFUynsclcgk4WUvu8VstsPF+ZJEjZiOdpiOd9GVYDTKMLpitlNgpaUsVyxWF+i7mqquuX5NM5nPet5Ul6myOo1Ex1wNq45tEDVBV8oztDeoOjdE3R3D01dfdUQ0uO46TKRUQussIQQyJgBx36K53iahhesiSBwe2W0jkjFz+2Kjml+S+mjovz9E2D3RTW0+378nmgZv4Bym+x1OSOdnPbAh2L7Noa2na73boqCtkhZeO+SGnRRh2xuGhG4I264Nke7GmOniNjrDmIjExk6VgnUZM2OdeVjgFsHG83SJpHCZOmt437D/of2AjC8b42XvuxQiAt49T0ugVZJgrCUfjUiylDzP3YEWovXGidMNxCm9AaxxaZ1HoxFlWWHMBUWxoixKBIJEJAgBdaM5Pj3lS558ki/7si9jMp1yfn5Glufs7+/RNJr1eoUQgixL2d3dIUlStG5IkoTpdEJdOfVNXVc8eHCfslxz48Y1xuMRd5+/y3KxYLVaUVUlr3rsCXb3dnn66acBVy+irhqsaZhORk5NdHFOWZVgHdK7ceMG+/v7XL9xnSRJqZuGoizIsoybN2+yt7dHmqQ8++nP8ulP/jrrSjNOJft7M2Y7exTasFpfYJGMpzmrZeF0/NrX6pAOvaWjKeOdffLJLlblNFa5FOayS38eQ5j7EKcQVHbLxYKToyOee+45lqslQjmWRxvN7mTKjZu3efnLHuL46MwF7jWKutKYRtFozWK5wArL3v4uZZmyWi5Zr1ccHx0hUNxIk7aOQ4xHYmQ3lCC24ZThOWhdT0OAqJeR5BCJRZvYpfbwnPqAKEUz1dq3vH6o3wchPOGJcNqAwb0Mb7Y4C+OqtRFQV6so7/RborODvBj4glJnDyc57rwQnS1gOKhtHGsrQWxQQOG5yP4k9AiRp/C936QA023aYUbVmLiJSOrZRoQCbJNULuOoYXtMh9sE8Zy0PO7g0xNVfK1mP5dDveWw7dhLYgjhnliyiVOWQMeJD/MlbVv3GIZ96Y8NR7DpMw7h/b359n8Hjq7WDYkPSFPK2ZxiF02lFHmek+d5ixSqqnLJ8LynTp5lZG1SOYe8ZTbizu075KMRq3VJkri2k9RJjqvVCiFcsGaapkxnY1dpzudfStSIPM/Z2ZlTFisePHhAUazRpma+M2Vvbw9jDPP5jDR1RvLFYsFkPOPmjduM8glGW/b29lmvVxwdHZMoxbpYc3jtgLOzM2azOVIk3LnzMnb39tjZ3QFApQmH1w7b4EaVpmRZzo07L6OsK9T9lNEUbr3sAJuMuX+8oilWmLJmXVka7dfeCJRVVMYgVUY222M0P0DkE6zKQLq5sNKpPaTt1i7OaBCIQl3XFEXB8ckJz9+9y/nZGRerc67dOEClllu37vDYE4+QZ5lLj31RkJBzsHudRgjWTdWumVSS2SzH2jnr5ZLTkzOKtebs7JQ0Tzk4PHRSfsQYDfeiDdIn2xjWTenCP93Wbm/Pk6CHO7r9jNuvLRLu3xO3GZB/kEaIPJeC12ZcZEwg2gJim2Oym+ffRB5+ffN06ELXxoYldjt8QYZmOUCm8afz7MFTpz5lHLYXPqVy1cHsJj7bQEQWWvfQuEKR8RtARH29rK12LEQoWfQN2NuQ65D4bSMiQwkkXEMwEDHjampuMa11esiWa4jaD8W8g76ybXfw3uGmGXJ0sL0IR/geV3CKpZIhbBv7cC/4qwgxNARahBiK+r44jDWoNCHVLoLeRcB3koRz+7NkWcJ0Om7dNF3fBdZqikJgjNP7q0Qyn03Y35lxenrMarVmf3+HL3nySVTigrXqpuDo6L63cQnyPGM2m5JmCVmWROsI2tTUDVg7AWGo6oKqLhiPQ41h56c/Ho/Y2dmhrmtOTk7Y3ztkMp6hZEpd1xwcHGDtHiqRHB0dUdUl129eY2dvh+lkhpQpe3v7TKdTZrMZQkom02lLuMuqpPBqsfFccPvhR7l25yZpbkE1rOsVTVmhCygvKlaFxcqcpgFhQyClYJSNme3dYLJ3DZmOMULhosz9gK3Y2E/x+gdcUNc1i4sLVqsFy9XSpRUXhulsymNPPE6Ww6//+q9xenKOEjm2Vjz6UMPu/JDJZMJ4MmM5WlNWBXmuSFPFbD4lz0bce/7E1Xp+cIRKEpIkIcucWjFWC/XP/Qt7zm0wdGzu4233Dw6BR7TbcFu3Z/D7uOufJeT/0u4Bj8v8ObB9wrstXUnot5SypymJ+zv8/ptuaB5OznBi4/vchvAqEC8JCNGF0AcuPzwlhd0gCMNFa9tniHR8/VPohfoHVdAwt1GMPC8b4zaCsA3izK7DudjgsBG+BGPXmfZvS0sMEBYRdJZRmyE+oGu3L3WZKAHAENkP+yel7GWhDWMIBz7o0R0x6ttN4vFtG3d/YG03o764QuvbJBBjXaF4JUTLDcZFlpRSpEmCkpI8zcjSFCVkF2+BIFUJWZoxnUxRQpIohTUN8+mUi4t9nn76cxyf3CNJn2I6G3F2saCqa3b3Dzjcv0aSJIzHOXmeIaXLpimAk9NTTk9PWCxW1FXN/v4e08mEpqkBi1ISIS11UyAQjMaZS6BnLaPRiNFo7LzKhEWlCqlEq1opygIEFFXhajJIwWQ6IfXFbGSSIKSkMYaiqdjb3ePWy19GVVecnZ5ycv/EpY9PplhpuFidgXTZhLW2rIslZSnAKJdTSirHQUqQ2Yh0PCXLJliV4kpxBgeM1pGbbRDWPpYWFosFZVkw3Zkw353xxJc8znx3ynJ9Sj5OyZYJVVFzfnrGR8//L8bZnFs3X8Yjj76Cnd0py5Wl0SXWQp5nHBwegk158OCYwttoJpNJW+TnhTKADpm1yyAwhJ2+JTzjGbRWanDXZbh1SyDdxvkPuMZ2kodjtJw6R4R08ltwzmXSQTwet07du2Mcto2BfTHwkg3NwEbu/iE4dC99reDOE6BrazD9gSsZcNebIl54f993OFQ0ayfDRyjaaJJggKsuec8LiW1DGOaXCQg3cNk9yQTnNRXqJweSOPRlDoRUWEccXLshMG5TFRarpuJ+d3NtW4QaVEZDn+ahBDV0ZbtMytsGon+uev0IA7TRJra2q4cRiGfor4n2hPDcVCJGDvmnKQJBVVWtN9JqtWK1WnF2dkZZlkwmE17+sttUVUVTF+wnO0wmr/TeRwtqU4OUXLt+wM7uHof7rgZwnmdIJdC6pqzW1FWFEAZjGrSuuFicudoP6gajUU6SuuR8eZ5T15V3KZb+GYfcXCCX9m60mvtH96jrmrvP3+Xo+Ig0T7l77y7WWm7duMPu3jWkUiR5hkodUdCeaGSjHCtcduHGGMqqYDweYQSsyzVSjCgbjTEjmqaiqgRVaZEGhPbJy6VFWolSqTs/0mWotV7qEliksDR205MQurTzwZ5QlqVzpzWaNEm4fv0ad15+mzRLuHf/LueLI1QqEcoymeboyvLpTzxNIsacnZ5T1hWvfvWrmc3HLFcu+NDiUpFfu3aNROXcP7qPMS731Tb8sE1qjX8fnp34cxND0BFETxDijATS2x1spMcfMlmecjjbhD8YQS2FCGmtvdQc5jeiD9sYsdjddKiBiNdmG776TU9zcVmCLd9931E3QKc+6pfcHG6qnigkO31XX+WyKerF5GSow27vozM2t7aFLX2/DNGHd8V93lZ8J9wXI9mYew7gEJqLRHY+3wHRBykqJpq+XV+0Z4hgu3sHRIutzMZGivChBBD6GteR6KlzzGa5w2Gm3Lhv8WfX327OwrwHxA8uLF8PDq4V/fGFfkmZgSeXq3XBcrlgtVyyWFxwdn7G4mLB+fkFxlgODvY5ONhld9cZLotiwWiScfPWDaSCRpdMR3N29+bM5zNcxTxIM5fWer0uWa0WSCGYzkYkyXVe9rI7fr6cXj9EtaapIwpZlrBYLFmvS6xxMRVN41Ris/m05abXyxUqUahMsa7WrNdrxtOx8+Ap1mijqZvauehqjbBuHaqm4fT8nIvVirIqWVwsaLRGSYlKXIGeRhuUymiosSiwCq0tRhsUEm18jqhEOFWR9JH2ImRYsAhjkNKlnzGXMIDhe5ASiqKgaTS7uzu8+jWvYlWfY2xNrUu0bjDCRZvXpaZYF0jh7FdJ5lzT1+USK0I0u8DU0GhDluUcHOwjU0nd1K3qaLif+toFdx5im9w26TTecwFiBiaWyNvnCGfN4SJvCRi8z/0ufYofd2ZC//x32z9DQaXtiE1f/RMzavHZFFa08TudFLKZM+6lBLC9+IjmtkRcV39X64HxWHUW/HgiY4iRUjsAQetFpIc5SHwaXgIi9KstBK0nzzZEHlJHECa37Y9wCdp84Xp3XeKydppOUhYh9qHj9OMxCUBIh9RaUuR0Pv63LthOSoXFZZ0M10JuE++Z2p8jOh2nbQkHIDyBs3YjalwpCe1G7c93i1R7EpVsqYjF5dVxG0+iZOcsEAys3r8BoyMknSR09pCOa1LS+Pv9Fu8mr3XmA5ct1T0nvO4aQhK6Viy2YNBuL0gJPptq3dSs12uOj464e+9ZLs5OvH98SdNAWdRUdcMrH38lh5Mx42lGUSSs1wumO1OuXbuGS48gmeQj5xaN5uT0hLJaM5uMOL845eToiL29XSzw7LNPo43zvQc4OLxOniZIz9RoqxHKuXtqrUkSS103zoV15gjPar1qgxPH4xFZljHOco5PjpnNZuhG88zTdzm+f8Tt27cxdUMlSrQ1JGmCaRrWPnajqitHMBLpapIogRKKyXxM1axZrc4p1gsWqwVFUyGNJUEirduT2iQUjaQ2CZaEpm5QKJdrCEeYbSgri0MBThXi9pXWLpdRXdWcn53x8MMP8eRrXoVMQNuS+8fPMN5rWFUXWNtQrRsWpwXPf+4IU0nqUjDZzTk82OPw+g5lfUbRCPZ2DxnlM0TqVE1aa7I84SDboywrksTXosB4j7AXkpJdviTbIuBwJvCuol3qmI7rD4xd+BcYG8+lW4tBkMjEp1Qx3khtIaTckI4gCAFG6+5ECyd2dKruTi3Vpuvw6V8QPoWQxzAywptN0zg1lupwkw3P0D0f8N9viU0hhMi7aesmygZDlFdxxMEX/cXpi0E9UcuzuQ6ROiLQUUaXwbEn5nmC0KqPhlx/pHaw3Yx3n36tI6uG+6lnxQ+3d5S51++IA6btWUdAQsCKtSFBYCRBeGIXOAXlE7wNW/MvaocEfW4n5ozCfo771G6Mtr99KSyMC4JE4Td91LbFEa6W4ATaF0kbiCAxxaQsHKgwt9HYOlYLROdm3P1oo0OKK3TUzpduxfaqqjk+PeHeg7vU9RqspTE12koa01A3NVIpDg4PGE8y1usFTVMxm00Z5TlGW6qqoalrjLFtZqqiWFGVS4rViqooOHpQI6WzZ8wnYy4WK1brNdeu38JKVyZSKUnubQBlWXF+tqCuGq5du8Hh4TUm0xl1XTFXc5qmYbVy3O5yuWQ2mzEej6mqiizLsFby6U98jpPTU4wxjMZjNK5WsRXOeF7VFVW1pK5r1ssV42zEeDICAXVTcbE44rn7v4EpS1bFObWtkYA2EmkV0iqUkpyvS+4dHTPdP2SyMycTEpEo9y4Z8my1nFL7d+VTbDRNw/HxMdPJlFe84mF25iOqZsU//8WfppZL7t4rSXPlJJoCqlKzXNTkYso4z9nZmzHbGbFYHSOUdWozqcAmJMkINc7QdYk1mkQ4b66whwNy38YZd2cj9Hs7B+3OdJw+JvwQkIRBRHmD2n0prMujJoRLBe+ZZWOaNuYq7FnRvsczpf41ncMHEa5yv1th2ySZ4cVxqVSnwpcdofGMb6tZic59O7ItTPo2eEkJ8ULOoEBxnU6xc7kaqohiiNVAG2oMKSIE3dfhDzliIUSPILTXBgPeJhoOEeqmJ4XDeNvGMHzHUN3T/R2IhfGfjvMIm3IoFr6wfv7yaOmhHnWbympoDxiqjMLfl6n2WokuIgjQEWTPU/mLXRLEaJZ6tK6VsfxhCiqvWBJr9bSBdgdiaAGr2z5p3VDXBUW1pK7XGFsjFexdm7G3e42j+6dcu3aLlz30cqbTKVJJlKqZznaYjkdujKlAqprVaoXVmkmWoYSkrkusaUDAZDKlaRqyUc7N23fI8xGrsnTqnvEYay1JqhiPc+bTqTeGpwgUJ0en7O3tcnh4iCX0u0IpxWQy5cGDBywWS3Z3d8iyzEdMK5rGcnBtycXFgvV6jRAClaWMJyPmOzOESijWp5ydnbBeL1ieXzgJYToBCWVZcHp2QtUssbZBppBOFbYx2NrQ1NYzWwnrasWz954jnY65wR32s4Q0GXlE6YLXWjsbHVOmlMQYp3as6orJZESSKY5O77JYHWNFiZAN54slSaloagMmY7FYMJ5MedXDr0ZbTSNKalMgdMLuzj57ewdIUoxxCeASKRFJQlMbpyYbpOp3+aiSjX0cM6BtJHF0bRv0ufb4XkHnCdOdqc3g1f75jM96e48VrRah+0fvmXCAeviSS+y3W3Bf6FuMZ2Lc8fngpVVei/4Ok9URpE2kvI1IbDMQubTZfUt7G7XI5oJeNthtiK3X/4HEEtoLlDqof4bQW1T6+vcXgqF0tMmhbL+22b8+Md1KADbsLNvnIP5tGJMQ3zfsf7tRhXMdll5ak16ctnR7RMXTEp6jIwKXcSzxQYnfbW1UiEi79BZ1VVFWK3RTIpRFCpc/ajLPePK1j6NrwWS0y3w+4+TsHHApNBApozxlNMoQArK8IR+NKMuS2jQuQ62xJMqV+VRSsrOzy42bt5jO5qxWBVYsXe0DJdGmIctcvEDupY/RaMyNGzdQMkFKl8dpXZTtGOqqYbFY8OwzLsjr4OCQuq6YzWYsFksslunOBCugKGrOz84pz87QumY6y0hzwWp9ynJ1QlkuodEIJIvliMZoV49BN6AMCEOiJNKOHWFtLOWiACMgtZRNwf3j56hpWBRrXqEEe/uHWOtK07pEch3SiyXnuq4oyzWjUUaSSJJMUF1c8MzdTyFkRV0vsakrJmS0IJEpk9mE1z75FF/6mjfwzN3P8OlnPoZVNZOdHeY7u6gkRzECk4Fx9duFwNkZGktd120MibN3qaGQvbl3o/0VI8Y+co+JgHOn7bj0vuQemL2epG471dSGMVjG57FrrGPaYo0HGC+l9M6v6DQbsYakf04uZ8hfCrykegpBt996ioQDHlHuyxBlq4eOfG7b+1sWZBORwgsHlQ0XZggvxEl3/ersD2HjX0bktu7ALWMNaqHAqQyR7rYo4c+LmAfX48/huOK/L0sJsM0oPuR0gnQYpKCgOjQAPq2EELH+x6CxrdterJKznirEPd5cs24zxESi48rANBrd1FRFQaNrF58gJI1tOF+ecHRyn0cfeZxRNme5WmKMdRXc5AhrE8rSkCRgbANosizBork4OQMDk/HIIfym8Qj+JvsHh9SNZTxWaCTL5YI0TUCkPrhNYnwUcFk6D6T5fIYQiqIssBbKqiTLMpI0Jc1ynnjNa1x1sqpisVyhipKT0xMQgul0zGKx5OBgnxs3bnJ8fMzHPvYr3H9whpQl+cQynsDuriIVIwSSRmtOzpcY643lqatrLCxY/5kIpxoy2ngp37Cq1hQP7rKua2SWIoRib1+h8gkIibH9NQN8plpDVZdYGtLMeTp97pnPsi4uMElNPk4oaudeKlUCNLz8kTu86tWPUNsli+oIspJ0MiYfuxTlQoQypQprXG0Na2uUsKg0p9G0Wqyg4LTRHmt7OmB+3Hbtx91sY8oiRERAzMGI3DKP0d6Nk92F/lwmebsvjgB1jF6fR7LtRfe9Rfx0JW47zUYfl3Zj6CDGMy+WOLx4Q7NfBClibxtiXcEGxxn90t4YkFy7QEo67mxAMAIiELKfbymWVoZE4TKre2/hQ28iZN1tmoZA50IVtFb0jFQmws9DtIW6xgf9C20E6WeoRoN+MNk2AjjciFsliuGMb9mU8foYY9rqWNZa0jR1qg/v/x0TBZcv3x8MY93CD6LCrYzFdEBY73kRJKIgBm+fozDOwE31xWtf9tFaKt1QFK5C2/nZBboypEnmVFcIqqrmV3/t11guK171yifZm18jkQnGgjGa3LgSm9o4Y6tBUzeVLwbjnA1U4jDAeDTixo2bZFlOUVQkifN8kiJhOp2TphKVuJw2xmhkkiARZFnOer1GJc5Oo5TCIklyl3dpgkSlGePx2KWHWF6Qj0dcXJyxLtccHx9zeO06e/u7pOkY3cDu7i6PPPoIq/Up6+UDpnPBfE8xGafsTHapqoa7zz/PvQf3aKwgkwJEglAKJRKsdPYt5VOGWG0wVUWmMlCaqmo4On1A9vSY6XROko5QKiWVsjUwC9up86RSlFXZxrTs7++zXF5QNw0IRaIsdb0kSRUyASEkWZIi05pF8YB7d0+4f/oMyciC0hiMy56KIwjCOscIYQ1VZSirktT6M+TPXZI6tZGNDc2tlsf2Aj63lZUdItQY6Qv/P4fsO/WN2/rdfh5COJe9bAGtPaIvFYR39r/31UctjorOvJSyxcO9szVgtodqs9+SLKkBeWmtvbEwZADEFcAI6WCtRUmBqxkQBui0z8Yan2TLedAoJagb20PC8eQ6w2J/wtvJ3iIKhvsCbMZUbDd4d5Pp+rbtfWGS2/KGrRgZ3hv6Fd7jfleJI0xN00RSVv+9/T50/QvzPVRXxZGO8b3xvMSqmDCvcSWvpmlYLpdcXFyQ5zn7+/st8er3Q/ZsO0OihhgEsslgb/BT5NcgSZSra+D7bozBauM8xYRAN433ynDjqWvneSKkm4OqrFgvVyyXK05Pz7hYnDl/+yQnTVPW1ZK6rBBG8/GPf5q6EHz5G343SZI510qtKYo16/WKNL1BkkBjKrJUcHFxTtVoUpWwWq2YT2fMd3baGg1pmpKkGbpwiHA0ycnyxNUuNwZB1nqACSlRi9QVlhGOqMkkZTqdorVhOnXztV6vfUqMHVarJZ/97GdodE2aJVRNySib+vl0KTxGWU6e7jLONKv1A+pSU0rLWpRUpeHkeMHFWYW2krwWTKc5SZIiZEKjDVVdYXRDIl06cpEmYAVpVZM3cH625O695xiNJ+TjCaPxGCGdV5MjBAKswDSasiq8Og7G0wlSKU5OTqlrSZpOQNVoagwlKoEsS5hMFLpa8OmnP8pqUSJTECohSRWT8ZQkyZEiBascQ+HrV0jpvKvCXg6Bl915TDak3nBSYoTY4ZRtqla6ZyOkjEcHceRyt+0Hkrjtp6Rpz52U7UNub8uNsxpAClfDPtgd2j6b4OEU3u0keGMMweOziep6BxtwrKH5zfc+kr6oRCSitTqytqehw93f3SBiRNy5g5kICYjowXbR/ZVO1RSrKrq2h7roy3RtwfU1jCmIcu5ep0uMn4k3UryZ+iJb3JftIlzMJVzW754oGo1lW1qRbTCUKGJJJdSuANo6BEVRcHFxwfn5OaPRyAdu5W1QUo9AO/1hKyaHlQousjgeAKLxt6sZDod1AVMYF9mMdQXqtWeOtDE0ummL53Q1E2qfEnvNxfk5i8WK9aqgairyUUo+TkjTjFEqaEpoakeAm9LVJ1BCYpWLh3j22Xt85jOf5i2/9/cwHmcU1YqqWjPzEcTCz11d16zXK6w1zGeOODR1RVWVJKnsEQtsiAIPqQs0WZ476UE6nJDlirIqXE0DKUjTnCzLWK/XrFZL1us1p2cnrFZLprMpSZ1hEk0iLZmv2eAC9M5ZLk/RpmRdloDGmgdIm3H/wSm6EWhjKRYN0lZIZWh0gZIJu7v7gObi4gghasajEUmSoNKUqmyYaEOxqrj/4HkODg7Z39tz+ZVU0kmMxhl8rYWyLFEq4eaNWySJQsiUg/2bHJ3fJZESSYaVNbP5hHycIrDUdYFVAjV2CR+FSBnnE7J0hCLBGuFjJCRSSJpGeHVb1qt1Ep/tbSrkmOt/8RL45plyaSQCN95JStskcdEygnGboj037p3b8zXFUrZQQV0V4Rzo4QOLpa4qQJCk2VYGdhseeDHwoomCUi560oljoj/YoHOhm9yhaDVEan2EHRZ72zNigGi9bnvLIm973wsFqQ0RvpSi1XQJ0VcP9RY89H1Lf2OufChKOpNMX0oZ9v+y79uIQk/CiMYZE5ahPSGO/D09PWW9Xjsu2OeQGap0nHjeSWtWRk4AnlNxasUu3kTYaBP7ObTem8SxXQ7pFt7HXWvdEqq6rqnqkvVq7fzwmwbd1JSVS0VdrFdUZYPFVxBrUppaoRsXhWwbCY1BGkmm8jYTQV2u0cZwfnHE+dkDzs4eYK1TAY1HI3Z3d7FSkGcZwrj+1XVFkii0bqiqgqp27objUU6SJQipUMoV9klTl/tIAEVZkGQpmcmoqjWnJ6dU9+9xeHgdECwXBQ899DB5PiJJFNPpCGNfwcnpfT760f+T1XKJFIrpaEaSKJSUTKcjsLtkKSipEXKOtgVHR/cQNmE0mjCbXefs7FmaukEqWJkKbQqqsuZg/5A7N+5QVQWL83OyLMEYWBdVG8E9GucIK6jrgqPj+5wvbpGOxkiVkJDQll2VroztcrWirkqaxp2l27fv8PyRYFEuWNcn5LMJ050pad6QZpKyLNA4ry6hBFhJnozIkwmmBquchsGdGYdHklSRZk61KaSgrhuMl1Di49e5y9PWbw8Eftt5uhw/uVbC2RZCRGxwrGbqiFAX4Nkxc30p3Xa2t+iMue9dDEXbFz+4oVYk4JJw/qqqREhFknbp5TcZ1suZyMvgxauPPNsXu0YJIdrI0zg53TBW4TKEtoF8PBXuIXT64pxbkE0qPWw/thf0Fgnn0tpXvwQEbbGNU/dI0S/fNxTznCosyJF9JH8pYpf9udsWKTwkJCFyMY5M7vUjIP8tvwVVUBArjTEsFguef/55Tk5OWC6XHBwccOvWrTZFdUDMoR91XSMsPkup3/xJAsrVzZVecgvBgiHnVOQd7bLW4kTgpqkxxnB2dsbRgwcsVyvKoqAsy9bOUddVSyiczcNJLcbWNE3piVCKNYoGS1U2LJdun7gVVqRJjkL5FBcli8UFWlcU61Nu3NjB6DV5vst8Nufg4IB8NKKxLshISeEIUVG6tNtlRb64YDyZMp3OSDInSeXeA0ZrS5ZmrFZLyrJwPvWpUwMs12sW6wWJSplMRqRpjqtOJ6jrEq01RbEiTRUPP/wQi+UZ61XBallQrFakMnH1DYQlSxPMaMJ6veb6jUMeffQhPv2ZT/Hsc8+Rphm3bo0ZjfZo6prjoyPOL5yr6nSUszPdoSka7j1/D2zCbLrH6dkx5+fn7O7NSRLl+jxKMGiOTh9w9/5dRuMZaZ4jE5c6JqglqrqmabQrGGQsq1WBNg3nZ+copdib7jHescz3FMviiNVqQVkVvu65wkqBJGGczsmSMeiEutCkmSFNE6wN1QdTVOKM30I6aVIbl87PefXIcADDgfB/R0Zn6J3Ly2wBAxaw9YhsD3mvnU4tG59pNcQ3QqCtbtVH1qf+Cf0I6veh1iFG7IHZsLGNRLrMv00TAiWTtm8h8WVod0Pd+3ngJae52ORUu46HUO5tItTw2T6n33c/DNdjSWH43IuFmHJfNqZWUrHBY6gz8oSxbYMBs9LCZdLJsE8w4PZfQGoYqq/i54xzGN/6XPgeVEghvXRAwi4B3HhDUtBatyUxjdbOwCwEI19YJk0zsjwQXemlA9sepvbdXpA0OILR1DXL1Yr79+5x7/nnWS2XlGVF3dQuSM1HAwvhEIBzQgjjrTG2Js1SJuMxO7N9lJS+lkFBaxC3hnw8JhEJZVmANb7EZkqW7TEej9jbnzGbjJBSuFrLozHj+ZSiLHClgAzLiwsuLi6o64amrkjU3Hv1KKR0huv1usFaqKuas7MzVssFUuLtIDVVXTEej5lMphhrGE9GjEYTtLY0umG1WlE3JSpVXLtxjSfMq7BaUBeaX/mVX+NTn/4U+3sHjMcTyrJyBuGjM9J0xO5rb/LE4zMmk32efvYZnnnuHtPpLocH+2TJCF19zq2xkmQqYXVxQVVUGANH909YFyussZwen5LlKZPpxNl9MkFVldy//zzT6Q7ZaIRSAhkien3J2LppmE4mGGNJ05xyVZLlKbs717h+c8bpxXM0zYI0zViuXUEfKSVSJQihKFaG1bJETy155s9Mar1vm23fZXXjcIA2G3vfSaObiM9pMvpnLj4bmyreDm+FR1uiYju8tO0ctylwbJdZuPcu36jDQ+7WoY4/ts9tO+Mh2rlVC+GC2TS6lzvsMhhqAF4IXrz3kelSK9j+bLuxYJHCGxelC3oZ6tHDpFjvliWEj1HwyL+18sfUVnTPAi1nehlsm5ieEcq7UITkVv3mAocedPBdNHXMLwTbivUXBDFhE70dJESX/0Q3fT3ftgykPZuC8gbeKLxetFPeqemstTRl3bUlpUfO/ZxVgQjs7e0x39lhlOfs7e2xu7vLYrFguVzSNK6IzHq95vTkmIvFBcWqRAjBaDTi8NohWZqSZ3knbYHTjXlNYEiDIrAOoVtnkzCNaVMi3L93j7OTE4rV2hmYhXOPdKmntYtyx7aMIMIgEsNsmrC7u8v1w5vcvHEb04D5WMkzn1ugjUXJBGMNSa7IJwnaFggsuzu7rna0NTz66EPM53OaWlNXLsPpaDQmT1OUdLaH9WqFNprxeMxoDE3j6w4/aMjHrhbDyckpddP4cpxun+RZSlGsOTk+IvVpty2GNM1ZrdZk+Zj5bBeVwGQ6ZTQe4e5oOD4yTCZT6lJz49oBu/sH/Oqv/irn5wsqXbF/uMfDL3+YV7ziFdRNgxCK0XjGyx96lPneHkVRYmvNelUwmUx9ZDBYK10EMpL5fJ/d/X12dufMZmNOzx7w9NOf5ez8hMWZi67OlERjOT26z3NJzigbo4RkNBqReMmzLEukEEwmEyc1+VoIQkqXviPPWN5bUNtzJjsJ850ZBpcHSiARVpKrnCyZIqyzJQSO2hrjjdsCrZ06SEmDMc7pwBEBt99kIAoi4JWgtvSEwnPXwVgskZdwc0EL0WICjytEwOr9sylcv0LFRI+d/P51SDzkN2uDP23AMZ0XVSAbbbu+D0EqCPgi4KuAy7TWvrSw3hqcN8QnoZ0XAy/epoDzHEGGDnoDMdbl37CNu096zl52HgGu8JyMCIXsiVlI2tTPIT+PcaMDQhpsN3lCCqToU/6AYIPaZahS6otzzvCntXYub9J5OGFBCOnzhiiEkk6vKW2bwTCWemgXPuLmfYomKVW0UD6viukbjmPYJiUgncEy7BxHXx2ybxpfo9i7UlosVV14faNEqYQkcf7wUqo2DbXWmiRLeeSVj7K/v0+WZd7gXHJ8fMzdu3dJ05Q8zyjWK05PXT6hpnZSSFmNmM2nlFVJPsoRBowGrGwL2DfWeELm4gC01gjjDogxlrKsuTi7YLU4p27WWCqgAaFpdIWQBplClijSPGU6y0lHKTKxZBPBbH+H+XzGOM0Z5yX7O9c4uPEUP/8vNPeeP2U8nlKWNTarWNsTxtTszvexomY23+Ngb48b16+zv7/P6ekZp2dL8vGIJE08QtLopmG9dgnn8nxEmqRkuUsHL1BYDU9/7nMcHT9AKphOJ+R5zrVrB7jy1CV5JpnPZ64CXO2zpqIQVqKEcu9DoKVDsk2taWqLaQTj6QwtYO9wn9e/8Q1cLBb8+q99jMl0zCOPPcKjjz3CycmpKyjUQJZkHOSHvPX3v5XjB8ccPzhy+0IoKq0Z5RnJaMZjr3qC23fugIWLizMm44zpdMpDL3+IZ5/9HJ/+zKc4PTllNBohrKEuC06OHpCIDFPXHB5eI89zLND4lBx7BwdYG4LKMrCC3/js57h/AoU+x6gFRsHewYy9ZM7ZyTm2blxOqNEeO7NDpMhoGk2irLNXCMddCiE9YVNY7dmcKE06OAJi8VoMXKS25yKwOPV2MFAHPOAYtb7tTylHmjtiIcB22YmFEC3fg9BInH3NWgva+HcIrJVe+dQxcw6HBSLmmGZtjWMrhUBY23osSp/qx3lfRZqECL8VRUFd1xho8ejQ/hDwSjzu3/SI5s0GBwh4QKXiZ2LEHCPGlvu1tBz5ZRbyjvpBTObDYON3xc+Ee7b1rSdK+sfiYvDxO4Z9iSWg4fs23ONeQLKJxxD61+jGqWsk4Ll+rMtsWZQlxXrtjMSrJdYaqqp0CNholEp8dO2IyWTKaDQiHzn1UNM0TkUmJeNJhpIJdd241M5NzYOjI4zWZJnTlZflype2zFziNQFVVXB+foq1hvl8TpZlCOGMkFIpEqN87qTO9dUY7QLOvEqqrEvme3Mms4SqLjBUIGoaq8hGknycsLO3w+G1A65d32f/+i5SWc5XJ1hlaZqKcrVCmJpGGcjh4VftcHArYzyaIpXi9PyYwtzl9GLC4eGMNM2ZTDJu37nJbDJFSunH5moh13WNtQ5BL5YLimKNUpI0dbEbQiqSJCfPRhhjuXH9GlJZjk+OuLg4R4g5QljqunT1EGYT8snYMURCI4RCJSmrdYGUC6bTKY3W6EZTlgVNU6ONZTKdMZlO+YVf+iUODw95+OGHEUqyt7frK7Htcnp8QlWVuMp8jeeq4dq1a9y8cZOj+w8wTcPu7i5Kpezs7PKqVz/Brdt3nM2orElThTENjRaM8oQnn/xSbty4yc///L/g5OSULMupTMN6uea+uUdT15ydnrGzM3fV5FZLdvf2mE5GHD04co4D1ZrVeklVl6SNZDLLWVULVwdhLUhVitEa3YA1ijTPSVSO1o7xS1QfV/QcG4UjEiHDQTjD4bx1WggIUvSw/GQPedoOWYbsuK2gbzphP8QkOO+5SA0eiMvA/Xt4rt09AnzyR2G7e4ONc4BFtvfXfw92tiClJ1HN6hhiu0L8/IuBF68+iiBMYvud/gL6i71BbZswd80FmSTJZornAH2bRN/CD4Pgtug9bb4mE+niosXbZidxaYS79w4H1vXFJwvbMtHbbAovtCBD24EM4ox0mWhDMr2qKlksFtx7/jkeHD3AaN1mi1TKpVNwvstOGhuNxsxmUyaTKTs7uyRJgjaG9XrJupgyykfOj10IRuOR9yhZslq5MRweHnL9xiGjfERT15RVxXq94uTpE8bjMTdv3mQ2mzGbz0B6oUYohOd+67qmrAqKZUFZlNSVU8vko5S9/esIUWFkgZZLrCgxomA6T8lGCUmuyEY1X/H7X8V0PuGjv/wR8qRgWSxoTIkYNaAFK32BloJbj8xYrTK0LkhSxc6tFGsluq5ZFffIcwXC1UfY2911qkrh5n65WqK1YTKdMBrl7O7tMp1NKEvnArter30ZSIE2lXPDvHWN2c4ElUgWiwvSLGW5XCGlYDKdkWUjsjSnqRtAUNUNsixRyrlzau2IaFkWLJdLsixlPt/xVcjGHBwc8PTTT3N6csLh4TXu3LnDfD53hnM/t3meO8RnLWnqyo6O8hFpmrK7u8vb3v4HODw8RCrFeDxhXZbO6QAoiobl0iHse+slh9cPuPOyh3hzkvLpT3+Gqqr4jc8+TbFya75er7l//x4jb0/KRzl7e7usV0uefeZpqrpiubpgURxz/c4ur37qYc7Xz/Lcg3NW1dJFP+uKsmyQ1sUiWCvRGkwo/GPpGU3djurqElg28cjw/LbnCBnf1EO9Q4LjrrX1/drz3UfQXcvWRpLKAN/AFndQuf3sW+sD4zb6HzwuZUuAQkxC0zRu70iXYytNs42CWUN10VZNxAvAS8p9FDhqY9zG7oI26DPOWww80OekYy49IL14gS9b7LidTWNR4Bg2dWiBqodnhpQ0liB6Rp5IAbnRp4G9Y/iuFzLubJNoOnE2cB/GuYMGKaFYc3p6zNHxA5/tc0KWp4zyHIHwhsjKceNlxXp1wWJxRp5nrFaHTKcz1y9Z01Ayn++wu7OLbiyjUc7tO7cp1iXleo3WDbdv3+bg4ACVOCPter3mmWee4ej4mFWxZl0W7O3tcf36da5fv06WZT5K2E1ZUZQcHT/g3t17rJcF1jj9/eHBLlmeoE2FVjUiqRCyQtsltQKNQdSW6WjC//Xr/4x0lLBaLajqiqJcY7GuAlsiQQtqoylMRS1qkrGgtg1NvcKahPFon6J+QGOmLMspx6djdua7JElKUZU0TU2eJwgFq/Wa45NjimJNmimkFBTFmjzPWa4WSLnGWsiyjKrWCKWY78xRqV8vIchGY8aTCVjnOScTRTYaoVdlp2IBVqs1k8nUeUmpkjzPmc/nPgEevOpVrwLgl37hF1FCcuPGDYqi4PT0lMlo7Ep0CkFZOWN2+v+n7c+aJEmz9Ezs+RZdbfUlPMJjzczKWhtdDTQaKAAUjnCAEcyQNyMz5A1/IecPUDBDihADgjNAC9BAV3d1dVdlVuUSm+9um+7fwotP1czcwzORCUGrSIS7m6npZqpnec8579s799l0SlNWPH32jDwfh3taBT2FOI6p65qvX3/NzdUlRbFG635+Io4ZjSZMJ4f86IcZn332W5qmo+068KEbretEmN0AprMpSRyxXNzy2We/oW3bUCeZaUZ5grUtV1eXAeboMyJvAwlfLDOMk+ADHCelRkcJUZQihdpCKMFo9tT5yG2r6fCcfvPyzQbQe79V6tufKg5U8n0NgQ9tx/BMOhsC2RBsftideD9gFWKnKAjcGUBTfffeUP8Y7EtorhI9TN9nKjbUOU0XMhatQhv5QLGyv89hv/vn8V3rCfC9MoUdpjXw4O+WfTilz8P8wxE/3I3sXQ/Ef8g4+OGJ9S9u9xWwQdlHGK6PCILSUUhC7heHxFZ/+P6035B+OjxDIXx/ncFj0zvGkKU+1I1193j3b5r7XQn3l/vefvg3wC7L5YKrq0viWHN6esxkOsYTJoJN65D0U8D9A1WWBecXF7Rtzfn5e9I0ZTwZ41XLpr2lrucETvqY0STjZ3/wU6SQrJZrirJglOf0l5H54SFHUqLjGA/cXF9TluU2lQ1QTEqSxFjjqJuW29tbzt6fcf7+PXXdIIQkSxMODkYIFeOcoek2xJklyyN87XC0COlJ84Qo9dwuzzELE2YQvMfavu7U02qoqO+htw5vW0Ss8KbCugohFMloSoSgs2uKKiNPxrw/f8vBwTF109CaFqkUxW3F7XLN1cUFxWbFaJz3UEkge7u5CQXUSAf65razJNmIxe01Hk+apH1NJwIvgwhPKIyFwqwJQjQWzyifhIe8/847Y4IsqDEUZUESRzx98gSc5/WXX5GmKQDr9Zq26chOM3Qc4awjSZPQQt1P/hpjQEoms2moh0Q99Ifi/fv3XF1doSPF7HDOwfEB3jnKsuDo+IQkyQONuBc4L9FREkj9hMSYrud06pAyzHbMp1PevH3LzfUVUgjGsxGx1qRJTF1VOGtp6jpAfp0Jg2lI2s6CV0gVBYcgYrQKcKZUw7UzWGdwrhft8oPIktxzCIMOwgN2prcTW9h6772haX54rgfWg2C7Hgpk97Ytw34fslUPFXyHob+HbNrW2dwz3MP5DXZpP4geXlMq1AoHTfWHbM592/K3Ah/dj6aH1/zeCe92/kBxlrsR+8BOGFhSd9j6/ufun9Duczt4aIj65V7hV247pXYZwX0Sun3D2786vLl384VULkQtYfF9J8OHeODw/sPQ0UNfyvam/cAhDJTiochU1zXL5QKP5dWrFxwezWnbmqJYUZQ1ddmRxoG5Mx8Fxs7pPGN2MKYoSt6fnbFaXdO0BUQt8UgTx5LlWjMazUnjHB0JYh0j1YzpfAqEVss0jUmzlK4zpGnKyePHZFkW2jXblqIoAqf+aISSMXXvwC4uz1mulghBKFD60EHSdDVNC2W9xkjDJMoZ5RHGljSmQ1mJMwJTe2QkkQROIaEVo9EIj8NZQ2drOtNinSfSEZODCUpD0zgSB23dUbcrOgfrxtCmGuFSrq6WvHhuiKOYpjMYZ6nKhjTNefb8GVq9JMsSnHdhlsC0AYs3LcVmjfeOzljiJKNrW7zvi5xty+JmgY5isiwnSVK8D1TWTWtYLlcUmw2PH58CktR77HYWw2G6DmsNdVmyuLmhaRr++I//GKWCqttmE8j9lFJMp1Oqqg4dNX2dzZiOoiiYTCc8OX3S77ulbVqWyzVRFKQxB8hvcDazbs6zZ88RiOC4nGc0mnBy8piubYmjiM1mzeL2Fo8jSzOapqasSm6ur+i6Jjxfi5bD4xE/+PhTrldvQ30q0lhMLzSj6DpLW1liqXCOXvBKbg2ks/00uqnx3vb1qdDQoJS+YxCHKHowjLtn+K4NGhzA7qG7X6+4y7kVPn8/E9lBS1KIQAb5AGTzUGA32Lz9Td1BTfbWvU/ds7/d/bbT4Zx3TmYXs9J3ZwUY+a79/S7Ld3YK+0Z0aE3dZg6wxca+6873Cy1C9MNxfHu6c99JfBuOBrsR9Q/4RXrHs+XfGS78PZ7zEEF8+EXtIoCH0swPoaTh331vfh/qGl4LNZAgpjsUlqqywNmO09MTDo8PAMtydcP5xRlNU9NWwcjFScR0MmI6m5BmKbN5xuHRhGwUcX52zs3NLev1DYejOTJyeGFIc80kS2kai3M2cNHkI6oqtKLqOKLuNQTqtiEb5WRZxvMXLyjWa96/f09VVVxdXSGEZLkuuLy84ObmGqUER8eH27Q8jiPmB1M8TR9hj4jlCOUleXyEFjGRFggncI3HdwStBOmIs5holGFsS91WNG0wRlEUoWJBmkY4Z4mIkEi0iHBdRFMEIZ1WdUF4xwmury8ZjyegFJ01KK3x3qK0RirV0zuEDiLT1XSmoywKtA6GLUk0xjZEStK0HabzRFGKMR1daxBCbvWjw3doqauCYr2iLiucg/n8gNFoFOYYRuPQ8VTX4XnoB7SGQcIgwBMgJq01bRumrdM0w0cxzhtWqxU2ioNoT55RVTWdMVR1TdPWpFnC02dPUVoyn89DzafuyNIMRGBZVT2D6/wgHNv19RWR1sybOWmWUdcVXdeR5xlCeBCeyWRCXVfM51PybIyWCavlhmJToRLQSRiIQ4SOvgAPSdqmQ+SKPM9J4xFJnGG78B1Za0IWrIPxcxIiHWQ6YWjmGOYLxB2nIHr0YBvG3Xv2huaWu88ofWZ3Fwt/sG28X+PbIJk7GYGUu+HSe4Z6157/cOC4v9/hPO831+wfxg7SGmDvnYN7qPj80PI9qLPtzuP13miAWOg7gAesQfQQy0MnFg782zk47n7J/f7vecxh2c8a7l/Qh7z3/u93U9H+PanuGeyHp5pD58eHmdD+Z+87gfs30XDsQ8vo/rqun9ysqoqmqVmvV+R53j/UcHN7zeXVGWfnr0OBMc5QcUSUeIS2ONHg8BivUDrl8HhEmp8ynqVs2hVetgG6MYooecLkYERUGy4vbqiLYGzX6w0AcRoFPhYtkFHoydYyUBtHacTjp49ZXN/w5s1rrq9vsB6qqkAImEzGxFEcBreynCxLUdpTNw35eEwUK3wnMZUkkjOkzIIQTGNpTYf3BudBKEeUOep6gY4thhpPR5pH5OOYOEoY5zmms1RFqF2gNcubJmQBUUY+mhIlMUmUI5Sg7moiUpwzlNUKrRKkVGFi+NEjXjx/wShLqUpJFIUCrTVJ0BDIYvCepm6ZTEYY44iilFE2ounCDIHHo1WC8zHWWLI0wXSWpmkREi4vziiyEU+fPkWN8r6J0mE6Q5ZlKKVYrVYsbm/J8pzZbN7rQMfBkYkoODNr6eqgGV2UG6JlRNU2OOtYF+sgmdmTCx4dHweOparEGkvXWo6OjgLmLUTf4unJxzlpesj8YM75+QXWeV68fMknn3zCplhTbgqSNOPxkyecPDnh7Ow9eT7i2dNnTEYHzCaH5DdjSlvTtS1KC7xve7xcYUxLK9rt3Ewa5VgLZdvunrf+WY+iUFAf2p4DOWKYeI4ivXUQ0BNy7j3P922Ac+6uotlubYTYQTr7QdpuG32dz3nUA4HeQ38P+xxe/bAS8lAgKe+8P6yjtbqD0gxGft/e7NODPxQ0f5fle8NH+3id77sF/L1R7fu/f4ctM+Rz+5H4Q5oDYR2264b3du2cd78Ycc/vf3Nn0wAPbf1r7/juQFd7lLm9D76TNe2ngPv44v0s4H52sH9s0KtZtSEyXy6XVHVJWRY8e/YkcNb4jtVqwWJ1Q5RookghcSSpYnYwYjzOieJAC21sR4QAJYgSwWSe0a0KGlNQVh6PZbW+Jo4TuiYI1ehYgfQkWSDHk1qS5inZKCdNU6qqwnYhui7LEiEEWZZhTMebN1+jo4SjRwdM50c9Dh+6JKIoJrDedmQyQ8io17hVaJkTp1HfWWPRUhIJg/Md1pU03YZyuUa3HVFqUbFHJqG4Y7sOnYw4OjxhPjng/bsz3r55j7cR1mhm00NG6RF5Nu0NTIgMi2LNan3Ocrnm7OwS5wL5WpIk/OIf/ALvPEVZkac5bVsjRc2mWnN7e8Pjk0OUAuENq9sFXmim00A6VxT1VmLW9HWCxe2CxXJBHCVMJlOePX2O97Babri9ueLs/VsQMBmPSNJ0ew8NUp1lVfVSnX47MFfVNc5YtBIIFxPHMWVds95saK3tsfmglz0EHlmWcnljuHp/xSgfMZnMMNb2nWya9XqFs4aPPnrZi9kkHB4/YrlY4Jzl9OnToI9dFNR1RZZnpGmKMYa3b9/zyasfEcmcxyfP+c3vf4kxLswgCYEQA321hZ5LSQgRMj2t8d71vwucC8Gm1IooilFSbTtwvPfbCfzwTA3Zv9g+v+F5e9jaePhgTuEDCLnfhlLqQ9oIvtkRfLAvHzqMPN8MIz+0jYdQhX3m0+E6PBSEfhOC8l0dw/eCj2CXXO1vX/TF5fvwzjcdxF2jTNAjfaCb4CHjPXjOcDHue8MHLvi2veu7OSwfPrT3c8+Ti3DLhXMLf4p7nn6IZO5/QQPr6AAnDF/sUPTen1NwzlGVJTc3N2GArKkZT/LQcSLh6uKCi8szOtshZNAlVpGgMSWLVUNrUkbTnCSNkJGk7jZ4J5BSI7SjtRWdrRFdqF189fXvefvuDJzm2ekrJuMDIq1JsyxcQSWJ4pgkSUjinQ5AFidYa1kul0RxMErGdGRZysnJMYeHU5q2pmk60jR0llgrUFoRxSnOt+FmVxFpkiKEBtchI4mzMM5iPB3IFkTNurqiam8QtkG6CkWLM46m9ihhOHtzTfz8AN+NWV6GbpssmZInU5TIwEXUVUuxueb6+pqyrFhvKooisJfWjaEzltPTU5qm4fY2tN6KPuUvyrDe4eERUkBTrplOp5RFgZAS27V0PXGf73V7y7JkuVxwu7jFO89kfkCepUgcUgWor2kaHp88wtpAZV5XwamMx+NtVjwajXDOs1qtGE0nRLKfKlcWU1dIQTDO/X3Wti1109I2Dd56puMpSZJQNyWnp6d954skThLKsiSJY9quYbVeYrqGx0+OOJzOkTLh+ESSjUIROYo0zapF6QjrCqRStF2HkIqL8yt++ee/pqwqLldf0LWOOE5x2uJ8KOhL5RAKhO9F7QdeKxecUjwZgfBY24ZCswcpVHAo3vbtmKHhQTAUiRXQB2IuWAjXR/R3nu0tZL2Du7+LsbxrzHv4nA+zkIdqg0J8U+WxN9hb0zIcy9YK7dbZ+32wGTvHELrX9x3cbl3PPhz2t+cUnLuTGQTSuiF72B38/sHdf/3OXMEe7ncfY9/f793j+ND53G9z7TcZogfBB8fzjdsVQ9FmOA63Tef2iem8dwykkfe7DvaPZ6CAbtrA/FmWZSjmmTDta/ooJIp0H1EHkZuqrrm6vGSzWWNsx8nJIWkas1ouubm+oihWdF2N9R1KC6JYYWnZ1IaqW4OekY4OUVGgOCjrGiECq6fSQb2sMw3WOIqiRhAzmRySZgn5KMV0IaqM45QoiRFKovoUPoqjQFnRNP0AW0TbtggBp6dPsM4yGQcKB+sMRVFRFhs8gijSAbvWMZP5HCEExnmUjPBoRBehZYRKYiaTMcvVDWV9Qz4akWeCNJtg3JrOL8EWCNMhvGCxbni7fsf7LzrSeMI4fk6cjYiiDOlVIM+zlpura373+89YLBbMp3OybIwxLWVdkaVjXn30Mc+ePgvDW11HkqQUdYBAri6viSPJeDIKVAVSsFmtKYuC8eSALE0oig1VWaF0aBVM0ohHyTGnpydAoOFo257sTYJKYlS/rThKydKEpjO0Jtw3SqmQndU1dV1hbVC2G6jOpVJEcYwUMBICpTV111G3LVVV0HWG8XhCnCV467HeMEsTrLFcX19TFAVKKeqmZLVcEEWa2cGUq9sLrG8pig6lc9q25fjwMGRAUiKURMcJylqcCTMTP/+jPyJVU87eX/Dv/vzfcXAqefrxhEH4fgiWBALnOjrb0LYNXdcSqYQo1sRaB2I+Z+hMS9sZrPEEvejQ+RRFEXEcBI9MF6gz/LbQGp7BQJ+zhRQ+sBewg4ODYxlgmbtZwIfZRJiaHpw+DIac7d87k3aXHge4U8sYXr8fJD9kO/ftzK5bajguAch7tna79a39+q7gzXenuRAKxFAEHarwgb5i6+3oUzf2vOQ9WGU/Ig6fGeoPIlBBeIEX4cbZwjAEjzqk/fsXff+Lk/3A1xDGh84kFzDp7b7CF7Xf5RQctuwdk0QxDIuYPvroKX2lxItA40BfzNkvTA+eX/VdS7bPDMqioCg3YRCqqKjqBmts0A1wFu8C4ZfWYRpZyYiu69islzRNTZbERFLSlBuqeo03LZEWtKZDSkuUaIQKk61KBly6MRXrchWKmGmOFIK6Z/201iK8CviuDKRjSlmca9kUt31XSuCvD/xLatvZFagEZK9OpsnyDKVEEK6f5GFAzhhul7csl8tenH6zDQbG4xFtW9O0OQhBOhqHNk5CK2eShtbEqmowJmDOeI13MVmaYn1DZyNc6/A2TAub1lKvK2wjkNmUPHlEpDOUlD2jacdmvQgsnaolzqG7KSgbQT6Kmc40xsb8wz/5PzDKZ2idoKMEoSK8DPQXzguSLKeuNqi6JklGpOM53sPcJ3gknfGURU0cZ0Rxr2AHRInC2KrPICKMtxS1ZZzNeuhDc7tY4JwnTTKiJA6ke0L1T5UkSdJwL5VLdBQUyZQcGjU0dWcxJhCjdZsNTbnBNjVpnDAd5WR5irMeqQXgKDcbis0GpRTPn7/AmIZ3b78MQjqi4urmHO8dVQU/+tEfMh5NsLZDa0kcJZQ95Xkcx1ghWK/XjPKMel1yc3WB6QyuS9AiwxLaSxEOKUEr6KShrguatsS4DqFEyAISjZCh0wjp+/b38LhJZ8LMTJQQ6aiHixzW1jujNzz7qqe5eACe3RrrIQCkh5oE4AOPkhChdjaQfPbWu7f5PfWO28FCqrcntlcmHPYhezu5LSjviefQy2uG+rbf2ibBh51H94PlHWz0ofO4X0vZ//ddlu8xvNYXklGBv0PseaCeNGpIlQbHsC+Bd9/L7TuI8O0EgxMuTI+XOY+SobjiREibrLFIJE6A3Ms8dhdj4FsSW1x3uOZ7pYutJx32P/wcUk7T8+mH7Yugcx5FdwrMsjeWO6fgesgp0ExbG2igm7amqSrqYkO5qWiaDtM5jGmxrsW7bpuRSBnhXRDRMK7DWQNa09U1V2cXbMolm3oB1jHOclQq0IlCKhGGuQRY21I2FfVlhdYR49EYJcIAU103OGOQQm+ztPA1GdquZLG6RukIrXKSJCNKE+hTcYsAF9oou7YNveQ2dIp0JtBAKyO3F7rrOkAwnU4C34s1+F7+0tqMum6xvmY+P0LrmLZtAYvzPsBGwhDHQR3MtJDlIwShgDubRBi3Zrm8wlrDwfSY44NTtMrAK9rWUNc19e2aTbmgbta0XYWQgiQPVBoOQ5woHj8+5cmT50zyOXXlUJFiNJogZURZBk6pWMccHB5TlhG2a+iMQ+kE4ywyTnAmsIDm+Zg4TXpjA14Edbeb5RVpGlM3FZt1iVYJXngSnRErhXUWrRJUFG2NmZCADN1f+EGYSlCVJVVZYkxDkmZIlVB3YVq8rSqqco0zHeMswXlP11Z4lxMnCXESUW02dG2HINQwXr54RtOUfPHFb6ibgtdvz7m6OsM6i1Q5xzcnZFlCZxrG4zFN3bJcLnHOkqYJ8+mU5XLBm9dfs16sWK4XGNshVY7ohXNiHSMjh7OODofwAqUS4lRhbBdsYk+oJ3pqXaUUcRL4yUJLatBZUFL3zyAfGMsQYPav3WtouW88h4EyTz8vxT5M3hvVvo44IBkIseUx8zI4AM+Oq833P4UM8wde9C2syL197c1J3Dn24KUeQj2+CfrZnf/eeW8zmu1aH8BS37Z8D43mu5rGd2oMYneA+0Xb/Wxhf55gv1IOgVpi38n43oUP+3De7b40KQLr5r2LIva+tO9SP9g/tt06AYdzzm45/YeJwd2EJTu91HutZN73pFqETCQUwxze50Ge1Dpc5+ma0Lcd/jlsF+AEKUP7pfMKQ9CtVVJiETStpWk3XN/cYH1DkseMsowsT9CJwPoOISxeWJyMcN7iCdHj4ma5JRX0HoTUPZ1HX+iLo16z2Ie03tRY69hsboMO8WiKVkHoxEuwTce6WHFxfoEg8PwLsZvQrKqCdbHGC8/Ro8MwAdw2VFUJQJKmHB4eonVElOTk+QhrbT/noNhsCuIo3rbfDaycXnhUJLB4cp2gpSNSJccHU+bzI9J4TFm0tI3h5uqGr19/TVkVCGlQkUep4KyTJOXk0TM265K2daTpjDSecHN9zXRyzGw2Y5znSBUBEm9NTz7YEClN1GdM1hic9UHv2Fq0jsjyBGt72m8AJbhdrDDW89XXr6nrmrZtSZKMuq05Pjyh6RqqtkWIGuM78tGYQKwGQim86KNorZnMZxTFJgyTGYM0HWmUbmnRb28XrFe34ByTyZh8PNpGvSHzDob88HCO1j/op9AlSZLwox/9mC+//B3OtTx/8YLzszM6A1fXl8ynM7q6pSxK0jTD+fCMzOdTDg8P+M1vWpbrW4SyvPrklMPTBK9rkB06EqhEI2XQnnDeIoUiV2N0FESKrDV4BW3bEccCIUPwGEVB9c2aYZrYbXH1wTEMz+59/PyhaHn/+b8fiT9kK+6/5oe05X4TitiRzu0jGH4A/fec1/BPbdOTh5f7xHYPO4bhHO6e0/418Q9MaX/b8r1qCg95L3HvwLftV+LuJN7+5z680DvHIoToM4/dhXXO4WXoBFBS9u/fr7bv8MD9Y71fQ9iHtB6+UexWCCbAOVE/Bb1zaDtOkt12ejfY46VDW1iAhNI0JVKaRGm0jACF9yus7UJWJH1wEC6Ms4PHKdErlUmaznBxeYO1HWW5IdgqjVIOZyxxqpGabdQilOz77UF601+7AKWFHnDwtofNhMOpEFU1bc1mvSRPRyhlWa2ucD7MSaRphuyFPaqyomoKOtMGWE2FYa3BCa7LFetyHSZhnaFum36eRWypuZvOUFYdujZ0bVAuOzk5YTqdAoKiKPpe/DBBvFqtuL6+IU4SJrOMpmrwOCb5IaN8zGZZI0YJkYxZrNcUy4Jy3ZKmE159/IKjRzOMbfjiy99zfv6ecT4mUSOwEYqMam0Y5xPG43HghFK7KeE4G9G1GtNJhHAIHF1XYUyY0laKQD/QR2dKCYRUdM4jlEIojRIpy3VBVRVYazg8PiLJNa3dgNe0xgaB+qZBRmHad//eHIqKSZz2GZXHdJa1KVgtS9omzA4kacLVdUcSRZR1cDYeSZKm+N5xN3VBlsfAmKqqOTs7QwhPnuc8efKEzlQ0bRkck1eYtuPt2zccHBzRNDVJnGCN4/Wbr5HKc3p6wvOXT8mnMW27wtiKtLa0XqGiFhFLhA6zFkO2o5SmqDZ05VuyF3Oc79sr+3vEE+opSou+ZhmuQ3AgQ3H6vo3aPZ8P2a7/lM7JQ/bgwWJ0b24eWvfB7dwjyNyuw8P2cL9Our+9h47924Le+3//F5fjvG9EB8N/Z53+NQFbTYRvWvbho4e+3AGv304t92IS1pjgGLbi8mFmAHYOaZthuB3pGf3ruy6nu0Wl/felFCRJ6LZJ09DCOHQNbQtJe47g/jHvn7YQYhtBR1qio5g4T9DnCn/d4asW33m8Bbxjq0rd67TGiQ5UBG0R1JYgDJnZimLToFQoMgvZEcWSJI7IRxnRJCdPU2QmcN7SdC0SixIOKw1tZwIFt+2oywJjLZ21mBYkMXk+QcoaY1wfvYX2VK00SmuUFjw6OSBJMuIoJopCJ9L1zSXn52cslovtAz6bH5BlOav1hqqqiOOE1bpEKc3x4TE+Ddni+/dnFEW5JXer65pBHKgoCtZFyWw25/HpI5SKWK8rUIpq0+KNY90VOAuL6xsUgn/0J/+YZy8+IRulbIoFZb3iD346Y5TNuL664uTxMUfzY56dvmQ2nSOEIMtGaB06W4aZgKauQyE4z4ITxNI0guWqCcNyWmO1QcgQySZpRt12IbvpBEpFzA4m/EgJfvObXzOZ5Dx9/oS/+ZtfM85y5gfH1J1BqZD1VVVBnOTbezHSehsoBSqLDinAmI6qqijKikjt6M4PD4949eol11fXfPnVlxgb7qoojpnNp0jhmM0mPX2Kp+0Ce25XNzx79hKtNecXb1mtChY3G06fPMF7T54n5FmKNYbV+obXr7/k7PwNaRbx5MkJ+UTx9fsl6+UGH7UoQZgvSUJwYGwT7m0UCId1LetihXXd9jlx3oduLwHWGWwXMnfnFd50ffBkcdvOooeYCR7OEO4XmbfdSN9gpx6CbgaYef/z+40mD+L/fIiaCCG2NODDsg1g9/7+rnWA/eP9rq9/0/K9lNfuD3oJucsGHoJuHjq4b/LGw+/9incyh+HCd10H3iHjCIHaHpf3fjv27RgiBYH3d6v0w/rh790Xtz/EFlSMBu6R3Uj9PunU9ov3QwHq7rHuHJHYHluAkQQyFliZUbYZHSOyTmBsHLSJW8N6Uwe1MCnIRxmTcUZdVmgVkUQJUnvKosRYiybUqkxrEMLRRZIuAm9jkkihx2Omkwk6UpRVuaVobtoGSUtjKqwTNHWFsR7nBKuuwHVnRNECpGI2m3N4eEQ+GjMa5cxmM6JI0nWOum0YjccIJTHOUtcNy9Wazoap1KZp++GjmDTNyEcdcU/pbYwlTVMePzllNp0znU75/PPPQ1fQfLZlAnXObYevtJJUdUlTV4xGCmcd3kmasiHLctqq4fZ6QRxpfvCjH/HsxQ+xLkBXSTyhqhvaZsOj4ydIIXn65ClPH79gOj4CB23fFTawgMaRxnQtTdML9aQjtJIBy08S4iimKDdoCRJP19YgAjwGgeK7bgyds7DxKJWAUExmU75+8zWv337NdDKhahukSJjPj1FRyN687XBGBkPYG0Nre7hPKXSWBX3qtsO2LaNpzrvLC4QQ/PSnPwsQ2HiKVJq//utfc319zYuXz4ljwXw+xZiOd+/ehHbhJGR5AklZ1BwePkJrzdnZBcePUqbTMEU9Go36LMUxGue8eHHKxeUF79+/xbmO1hQ0XUXnaoxrUNoilCf0S7m+JiDpesxbR4KiK1hvlrSmQcUa4UInFlL28qsWa1wYiLcDZHTXxuy3aQ4Z+mBK7kNEW2EocdeZ3M8IHjLwW1sCd6Dwfbt230ENdQPEQw7qAeU1djK//6l6wje9NyAs++cR7Nnfgp7CdveCcEK239ED0MxgbIcDvxul3z2h/fd2RZIdfGSd7YXUO5Qa+pG7bRFayL5Q5AJ0sh8NDLu8nyEMkJVUQ7tpKHAKKbZ8NM5bdKRRvdZD1Gvy3mlBvecDlQqR0KB3PFyLJE24WV7y+69+T57n/I//9/+ef/kv/99YajpXoaRguVqzXBSsVhWr5Zq2Lak6i5MOIQxFW6FiyVgndE1wIjgfOqeswAlN1ThwHc5scCaiqyVZnpIkOalOaVyNJaJslthG49oI1xrybIJA0DlHW3lM2yKVZmGWdK1hMt3g3BFaCZomYrlc0rYhjR+PZhjj2Kw3vXxnKGh77zmYHwX4LIo4OTmhbTuU0tR13WsxhKxsOh3z6tWLQKVR17RdgIes86RZwnQ2oesa1sWa5eKSOJqCsCwWK5IkoakbVrdrnj17xg9/8Cmz+QypNE0nkVojjWWxWPL7Lz8nyxSjPKGu1xTlmlE2wxlACNarFZuiYO4cbjTi+vqak5MTRlmG8A7hd1BAlmU4H6jIpYS2ayjrAoRDqJjxOMPYDd5q6qZjtb5F6ZjZ9JDr60uapuW6u6EsarROAUGW5mgl6IwH77i+vuDw4BilNLbvLooijXUiXOcqaGp0XSj+Pn/+gtPTp/09H7iiDg/n/Pa3vyFATi1lsSHPRwjg8vyCJ0+eEkcxOAILa5YQ6Zif/OhnrFYr8J4kjrGmo9isef/+HVprfvzTH/LDn3zaBxo1jW2o6ipoI+goqKR5gRYKoRRG0rckt9guzNZY13B1fcbJ8XNOjp/3U7sqtL06saXHcE7guGtDhizym2zLfuYwcAGJvhDtnOltz2CDdvZnnwJ7v4lla5P2jPn++1vzeA/qkUrdKXpv7ZNSAfK95wS2QkL3kZi9be7O78M5i304Pvzttut+l+X76ymIe150+J0PvdadC7NfnP7Auz2cXQxZiFIKaQccH6w1SEmI6LdRevhiRT/ZuH8MA9Y4rAd9wVrsnJDbu8jgt7hlVZV471Byp2G8LZCru44sbH/HjBjEajzr9RJHxO1myeuzN3zyySe8PvuK8WFC2VS4LmgPZ3OPSCIOn+SY9pj3b96z3hRgHSoSSC+JdWhVtK2irTu6pqNrLa4NzlJIiXOSzbqm6264uVqh44g4ibbRibUm9Ly7UCCN0zGRSJhMJxwdHzOZTkFIbF9nWK1vWdwuKDYF79682daSojhhtVyS5xOmkzlSaOIoJstGHB0ek6YpaZoFJTiliaI41COqGoEgnaZoHWCioihI0oSmbehMSxxHCAFt26C1YjLJaVpBPtZMJwlNu0FJKKui74ayzGZzfvqzP2A0yoM6n3BIDbFSuEaQjxMQHWW9JklzyqZhsYrI05wkntA1HR44OjxkNJkglQydUwK6rqFrG7wNNSelQrYEQR3M2AYhPU1V0bQJztXE6ZiTR0cgIsqm7e9bQVV1XJzf0NQeZ1va2jEZS87OzkjTnEcHJ7RNQ7FeoZTGdDVHh4/CRLgLutWR0iSjlCRWrFYdTV3x8uVLfvaznwGi10OOQFiePXtGHEecn7/j7bu3WNPx6tVHjEYjnj17xmQ8xXlB1wXuJWs6wDObBQ2Om5sbNpsgPBQKvZa2Nbx794bZfI5znrquqdoSGaXM8xzvWzbFDQhDpBVauBD5uzBwKSPAdQhpuLm94OrqjOODZxwfnIRgTinoh92cF6HQLATOmjtUDvvP313N48Bl9GHh9cM64/DaQwXdb8ok9vf9YYH3XuQfDuRDFOVeVrJ7/+7+729vWL4Nlblr7759mPj+8t1rCvuF4Ht1hYd2dd9z7juEhwor+78P21PbukGYXfA+iKGzLdGEowgojriju+z9hx57fwl1iJ0jCAnPMELeqyX18M+QKiqp72RBw4xFOH5358sN2QUkSYTWByA942bKZDJltV7y9ZsvkaplsTqnbBYYV/cKX5ooHTGeTlmtIsraICOBlAbvBSJWCAFKebJYMyLBdZ5qY2nKthfD6YvhjaRrLZRhsIxQnwdC9iVEUHlzVuCM4HCeczB7xKuPPmIynRAlMW3Xcn7xll/+8s+4XdxiraFpK4zpQISC6Gg05eWLjzk8OEIig3ylTkK3iAMtNYLQyz0ejei6QBinpdpe//VmjTEdZVn0sJ1GSk9Vb2iaBq0lSoUOoqYJWVSaRWil6IxhPj/i7/3dP2F6cIg1XaCWRmK6MNFbt2uk7khGsFotKNsK2Uq8N8zmc8bTMVk6Jc0ynj4LVA6boiBKIozpEH7XUiilQipNW9dUTYN3LrR+djWdaUJNIM5JE81knJOPZhgnmc8PePP2Nb/+9V/y/t0NRVFxeDDnyePHACyWC969fUMsFONszGZ9g/fQ1BuEc0xnM5IkAR+jlSTLc46P50gpsE6gowDNOQRSRzhjyLIcfKA7ubq+4q//6tccHM6Zzw/J0hEHB4fEUUpdN/2zEzqRuq5lvV6jdcRkPKXt2l7QKWhvRLHm3fu3XHx+SdcaPv74Yx49esz8cIrzhsXyEmsdTXsT4M/EhWdCRUilMCYEePlIc/n2lrPzt0zHjxmlY/J81GfoCuE91gpkkGfEWbNtBLmrP273nmcIuP9uYO5BmPobbNb9GsSD2cC3zEDs//TebwvNd0ns/NZG3bGH/kMb+dB+dvsItuf+/r+vE9lfvv9Ec3+BXf9P7zOI7kfh/sML9c1fxkMHvuMQElKEIRlrMaYJ2ZUDYQWg+66Yob/3vmKS7LOI/T3u44RhnYFqe3AEpp843i8mCXZw0HANwpAdvRHbwUwB5rLblj+k4KNRRpbm/Ppvfsmvf/VX5COwYoVlg449CMNoPMG7mrrxlPWG1nXEkUTEuu+GCn3SpnUIJ3pKacEkiolSSVu3tI3DWx9osr1CIPvhMxlmS0VfvHNw+uwZP/s7f8CXX33J1WWAN/J8QpoGo5YlEeN8yqOjE6wxLBa3OBN48sMkqaApW6b5bKs0tlwsqaqKLMtofEOiY9ARTVltC/ZRFGGamjbS5HlGVW366VpJmiWhhVJpymrDcnnTQ3WGrlniXEOep6xWJdbCKJ/zB3/4xxwePaHtDFE8wjhL50p0Krg+u+DP/+LPOXky5+BRCpFilHuauqZsLzm/+oKu63h8+Cmj6SRMn9d1aEv2FiVkEM9JU9qmwbQdHkFsPXVbU9UFzjcU1Ybl4gbvOp4+nRBHiqYqSKKUxgbWVIEiVjl/8JO/x7Nnzzg6PuDk+ID1esG//v/9K25vr/jafsHj42OSOBh5JSPWq2uqasXjx08Y50co4WnrNRITjL6BLI/6zD1If0qlEcLz/s2XvHn9ms1yyXJxi44i1qsNUmhAYrqK1WqFc46Tk5OQFZlAue2MJY0TbNcRpRlfv/4a7y3PXjwNrcVRxPXVLVIpnj59gRQRxnaUsmGUtDjTUNUFHofOFNkopek62k0JoiVJYzpXcXH5nnH6muP541DTSSKUjIIdEAInQUiD1yHjtHZgnw0wkHOEVuBharqPkLc25AEoZj8KH4LCh967H9QGG/EhL9s32buHUJS7tu5exuHu7vPbl4czlfv1kIfW+able8NHD13cLbbeN87sRtgeKvCKD7YVTuDua57doJt3AevXOhhG70wfvfcTyEpv04NwPMMX91BWMqSWO3xxl2KFdfZ1GowxCNETirn7nl30Ua3cCl5sWWN9EJ5JkqRvw4yRPubl01eMs5S/+ut/z7q6QEZBijROPHGmOJzlFGWDIWDtCDD99KrUAiNMGKhRoeAmpQjcRNoTxRo3jrAddI3n4vyGWKVsp6icwTrw3m4zpYP5lD/6+R8ym0/5l//rv+L2+pbzs3PiOKYoCrzwbFa3YCGLM0zehYltDxU1zkISJyHL8WCNoSwqoihilI9p25b1ahMyBSX7CDAOXTWRQvXqZuEbd0RxjPeOKI6I44iDgxmIMLTYdiUbs6JoWjZFoIQ+PHjEy1efcHx8SmsEcTymsyFqjNOI1+9+yxdffMm6vCarLHHmeHw6w7mSui1oTEVR3xBHI16capIkpTUdSEE6yqnriuXtop/GHhMTMgXrBJM4pTM13rcYA9YmcOvYbNZ4Z8AZNkWFMY6i8bTGI1H8/A//mPFoTBLHTKYjjKlJk5TZdM7Z+3eU0uPMjGSUoyVEWpJNR0G8qFxz1lQAvQPN0UqyXG6YTOa0TYN1iqzXobam4/zykqvLSw4PD/jH/+gf0xpDEsc0dUuxrpBSUxRFaKgQgixN0Arev1+xqksiVbPerFGR5OrqEu8dJ6cnHB8fc3NzQ9sF2K1rLdJHRNEI5TMicmKR0/gYbzuU0OT5iHEsibOE+tZTd4okDUR817fXXN9ec3BwhHW+byWR/TMuAiToh8aSANsNolnOSaQ0oTtr0DhnX0Dr4Wh79xpbm3UfMrofEO/Kzfdslr9byN6+D3da9/fth+CuE3qoTvkQyvJtsNX9TOf7Lt89UyA8toI9MZs7F4ntMNdgZHcnFDzr7oT2I/c9r9ob6WFxzm25gIA+6h5j2obOmt4Ds9uPYLvh/S912Ok+NDXATuHYwt8BJfJbUZMt8+KAJ4qgjTp86VKKHtbYOYQBSgqiMn4774CHSEVYA4+PnzD+k3/Cl1//Fav6LXV3g1MlsVIkiQ4c+l4htdw2vcaxQqkwHOc8GGsQXhDriHE+AuPo6hZhIM8ztEyDFvOmwXsFTiBRu5tTeKIoYjIdY1xLkkVY31LVjouL91RVGSgpuhZrQ188TlKVoZgYx0lvwAVHx4+YH0x7rLnh0fEx0+mMsizpRIdAUFc1SRoYXcHjndgS7HXGoLTCdJbS1WR5ihCCtmsRyjOeJBTFmnW5QscwlhnGeNL5mFcvf8inP/gpxoTZAONCi7NxnouzC16/+5raLMinnnVxyWEywThP3TUY1yIiTWNX3Kwv6KhoTIU3oFRELOM+a2yp6kDHIPv6VucsXWtYrTdkWUY2TnCyYXo84Yvff87kesR0PmWzWbFe16xrS2cFR4cnzKYHxHHG4eEBSawwtqasVvzsZ3/Ier0h0YLJdM6jRyccHR0hvKDrTN+BZ7i9XYRefimYzeZoPSKKNVqrUINBYTeOKNJ0XU2aJJyenjKd5NgeepFCoVRMVbYgJGkcnOF6XZCkmjiJQIQifZKNmB8dIZUgyVIuLs85PwudTre3t9vtxSqiqTxdXbK8XeN8h7cKrEKLHCUtOMk4y9FasnEduU65uapZL1asN7dcXV/x/OlL8nyEVL7HO0MzRSjYDgVghdYDhb0l6D2Hdt3Q72S3ETeIfij17rzAvvEMQeJdanwhdlnGh/WAvV/vbfObYKgPAuIeTRlg+GEo1gm315ba78yzcxZ+OOZBV3rPAdzLUv5zHMR35z4aWC4IA14IgRL7Hraf8PXhtXB8OyhH7hVqw0Hu6BCst1s9Usfe5LJU2H6v0ofIIE1zbBQT2T5b8OBdYN703uNsT1q3J5ZtbTheZBgACgZcgfDbIBrRD5N43xv5iCTJtsc8oGSh9XWHMQ4c58PFDzdT3HPaBP3ZYX8egU4ynG85mJ0w+lnCV28Tvn7f0NiWoqyZtA0Wi4ol6TjDAaM8Io76LMSDijStF0QqQivNq1cv2Cw3nL0/QwJVvWEyVrx49Zi2tpRFw8XVIjBHevprJJGRxyWO1xdf89HHH/FP/qt/wJ/+23/HanXLerUk1knQFU402ThjuVqxWNyCcEjtmc6DgtfTZ4/xiJ4SArIsZrNZUTctxlpkEiMjBSrUhkzXkSYZTdXiXBDeqTYl5aYOesJO4ADrKup2SVXf0pmSm9Vb6DpOT18ixQgtx7x48ROcSxBeEunQD4+Ed2/POb86w8oIrwuUWmEaKNcZh/PneJvgRgIVmSAg1C347OtfohOFIkOLlFa2bDZL1ssFaaJYLq+ZjGdYJ6mLDVdX5zRVTRRPSCY5y4uC6+qKNde8XQgmN2PaNkWjefvuDSrO+OGPfsZ0dkyWjYkTjVSGNM1pXcuz55/yJy5ls14ym8R4a/jq9VloSTUdaRIRxwnjyYjOOG4WS6rGkAnXwyRBf8K7ls62WCMxXUuWRkxevMR0NXW1YbO+xRnP8xefcHyY0hkoJk2gZNBQtyWrzS1PXr4gH40DNYoNg3XPX31Mmo/461//muvLS4SA+XyObQy2MxjnabowyXxzfUWeKyI1RQmL6FpooNkETW6tHOks5vHpnKvzNavigouLN5y/P2WUzQJ/lfYY1+KxWCO31UTPMMDmCAOnBucMA8VM27VYY8ALpAp6ys7ucaQB3u8oxaNBZ3vPfm1hJz9QjMCAQAzmZRto7gfIDyAUUt5zKvi+U5K94baeOVb4fbno7RlvjfqAaGi5RVMCouKGg0LszX6F+icf0H580/I9RHZ2PCGD597VdHZ4POxwN7ZvP+w9d2c4bGhvbN2Hk9w3uEPNQOsIpfTeuLvb2+9u2XlPhgrrnXRN0EcafUSPByVVn67KO3DQcIxhynV3DvuSeLtzHTIJuff3QCgnkGi89cQ659HRKevymsvbAmsDa2lnO7pmgxCW0UgQRf3QCyI4FycIYkaa8XiOR1FUTSj8asFsNGI2mRLphLpsWa9Lyq6kqrow1Sp1oFrOY86v33C9PCPOBM9fPeWzz2dcnF2Dlay7AokgmyRYb5DAOB/TdDUqgixJOZzPUTKokdm2xRhDWXnWmwJrIUoSdKxBORrTIlUQYi9rh1Ixk1hSlA3WgFTBIFla4mxEnEDZlLTdirpZYV2JkpLl8pZRHvPyB68YTw52pGn997NcLmibGiVTbm4qRARJmiC1pFy3CFsQ5wkCjRAGpS1lveJ2ec5ieckoO6It1wgrqeuSolhwcf6a589f8OMf/QHj0ZzlTcd6tcI4z2rt+Prsitdnv0HGJVEqKOo1n/3uM44nn2KaAGfNZ1MODw6ZjufhLpCSKJa0piJNUuqypSpbtEpxTvD1119RbhbkaUyiBHVVMpmMSfMRUZwidYQXCucMUoGOwu9tUwZ9AhRdW9O2DfE4R9qQxUZaUZkG6wz5KMVXhnysQwee9FwtrrDeM5tOCIZK9VmxpqwCA+zzZ88RHq4uL3n79h3TyQxrDca2KBWMdtWURFFGmo1ANKEJQUmqzQ35WDEapTSVxdHw+MmcL35zw+L2krfvXnNw8AitFdY1lNUaIT15Nt4WeDvT9cJBwRkMMy3eOeqmoSwLmqbu54yiPioPz77WmjwPOhV3YaLdMxx+9vTTwWhs0Qjv7iIRD2Ufd2sR31xPGGogWuu96H/nEAYthsF23a9D7Ncz9ovZWw64vffcf2nlte2FC38w4Onb9ErK3ZnsG/e95f6Y+bemNH0qdL/gE2Aesz35wXA/xCe+3a8QW6eww612jmv75fnQZjo4hFAwVuw7hfs1iA/wQyG2WrKD0wizFMP0tkfpGGdBKsFs+ohHx09p7YZNIxBeo9DUxuK9JUuDfoGzQSwltF4ecjjP6VrHKJ9xc73i7OICvCdPEtJRxrpcs1q8oSxLlIpQqWGU6t019540c6zKC6Io5k///f+XOM65vL2iahtsC8oH+MQVhpHIUCoKHUM6QkdwcnzMdDLBdYambKiLEqVjVuuCoqgYz6aMJjmPTw8xrubs/C0OTaRjhNcgPM6l1HUVCoZK4FxN2xlubm+ZzVM2m3PK+gboGI1iYj1G+JQ4SZnOZjgfnAsE6cqiWAcNiqqkrRtcpziYPePoKOH2+pbKFWzWBWOhQUao2BOphjRxWCrenH3OkyPDJD8mEgpjPG1T8qtf/QVKCg5mB2zSDW/evMM7iCLFzfKaq9V7mm5DpFuklrSNIZ2P+eiTH2DqBC8d4+mcqK+J5fkEIT3W1kQqotysef31ay4vznn69AlaKQ7nh8zGOeVmhZb093C4T7vOsNlscM4znR2A7+jaqr9nI/BhjqGpS5ztKIs1NzeXXLx/y8mjR8znh3SdCVoK2RjlwjO8KlacnZ2hI8lsNiW0dwvKMtQxmrqhrhtmszlpkjCdTrm8vODk8QnWW+q6ZL1ZcHHxjqJY411NWUvSTDGSCmsUeTqnKm5JMkGe5xwfS6appi0ilhcr3r77kvnsgLoOE/C3i2uEcKRphhAS7xxdT8rYmX4yv236bMEFdbu2ou2avg4Z9w0hwf5kWcbz5y9J03Qb1A3dS0EqNNiZHSMCQS9d7GCfB23jnoG+/95DNdW7LbS7hpaHh8z8dr/3ndH9/UNAWthvyX0ge/mm5XvRXPjdHwRO8Z1BHZzFkCVsX38Ij+PDgvX+yQ2/i4GOtk+RAkFaP2h2j5xv+H347G6oBcTehOM2U+h/iv5hGIy3vjfBPGzv/pzFroYRevZ37WbyznGEkf4+c1CDE1UomQAKQcZ88phNdYtbtUgBkYIsFRSbAqkkOoqoTY23DnpBkcl0xmpR8Pbd+2AcfMvBwQxrOs6u34dukUiRzqJAqSxDNGmtRRHwyyz3WC9YrRfYqmYWw2ga4XFsljVtW4UOFSF5fvqUsqz44vdfEqURR0enpFGGaSymM1TrgmpTEaceJwST6ZQ0T5nMcsbznOV6g6GkrS1pOkLLBLxnubY4a0K3lmlZba7YlAsm0wTkhM6sQNQoDVEcoUSEcDFHjx4xnk63dSzwYB3r1ZL1asnl5SVKSf7oD/4Ok5nGmJJUHnA8N9wuliyWV1ivMbYjziIO5gnFpuF68YZIabI0I0kVOhY8eXyE+Pnf4f27N7RVw8H8hK61ZKMRwjnWm0tas0DFHcaVCOFQKugcS6WZHR7QNg0nR4+Ilca0FS5JkF6gleTm5orlasl8OuHF86eMRyOsaTiYH5ClEavlDXkSMZ2OQ3TsPFJpNkURiAbLDTqKSZOgUd0Zg5SKLBtRNw2b9ZJyswoZhQx1pdABFhh1hYCmqUjSQM89cD51bYfWMdPJDO/CrEaW5yglMU3LelOQ5Tk/+ORT5rM5dVNhbEVRLijKBaYtWXUecOgIjt0cg+blx48pypKmclhTkcQxz3/wimn+mP/P//KnXF6/5fPPY96/m9I2XRigw4Pc2YbAmOyw9q6WSWgOcThvQDrSJCOOQ4fS8IwmSRIYWHthqPBM9vULP7Shh9eUut9ZFOzJ0KI82IP7du1+58/+uvu2b+eU3Afv3fnsXu32/rb2HcJg+9QDg8PfzSV8L/joGxYpHkhv9gfBdgIP3g+j4WHtcBJ3nQjsHI3f+313gj0+J/aYVgcPLnb7R7j+27vrlIQYJhL7C9V3D6m+NhD1bKC7KB+GrGg/05FSBtWovRrJIDcYnEKIsLYYaO8spZR4JxBopJfgIkb5AZPRMVI5VGK5vr1EeshjwUZV2NYFyAmoyor16h1lUeIcrIsNOtLkeUI6UVjrwAkEQUawKkuctKjEI5XENh3OQxzFeBloGeLMEycgo45squi8oOosOlV8+vEPGMUZB9NDvvzya6y1jKMxsYowjUVIgspXWaNQCK+IkojRdEQ2ihlPMtJxxKI0dL5kU67Z1CvG+QFH8xghO2xd4oWg6wra9pokceRZjKciSSXap+goCOUUG4szhnw0JkRzFiEMzlk26yVd21Cs1xzOZxwcTphNA4XDOJ/y0csfsN4s+M1v/obXX7+mqDY05paDRxH+xNJ2ls5IVsUlV9cjlnKB8gphLQcHU2zXEumY2XREWYVBrNdf/44v3v01+cyTTCwyNmgZBT0EJWm7hlEG49GILE1J4oS2seANUihWiwWbzYZIKybHR8SRJtI6EBe2NfhAIPj169dEWgWiQyk5evSIpqkpisA8e5COGCJ6rSPiPMW5EFBprTk4OMI7y8XFe5q2xXmwTqGbDhklSKWpm7IXdDrpny9BVdVb7B3vkICSgnXTMhqNQmHXOZq2AWEZT2K8HGPtGEQVRKKMoShb4lTTWc1VnnFy+oLV5oquazieH5BkEUqXTGcJb26vePPOo0UKVtK1BqkVXrhtxu/9rmMx0nGfRYQmAyEgjRPycc7BwQGTyeTOMzidTjk6OiLLsr47sKNtg/Pah5Kc23dCgxXss4etxRuOhz27N9g7NwAed5bBSO/aZ/e0Y/zdobydYxAI7jqah+CrIbj136Af/12W7z2nsPdCSOX2y+S9AR0KI/0b34CD7dbZ1zneX/Yv2H56JXohnf2T3TfWw/HuF3cGZzF8oQNXklIKFe1qALKvJ+xDU8OXPdRNhm0HQZ7gYIY5he16frfu/hJqAgGXRGgUCZGG2fiEySRHRoayMLRVRawTsEsWt0uEgM44jPFMZwnTg4TDwwOUUlhvcMKieiZK0+Otm9Ua13V0zuC8IJICnWokAq2jUBAU4KTBKwlJQ5aMEElEPMoxnacR10zSJ1zenHF28Q4UpFkgv1ut1kSRotiUVFXDZDInTlNknnBwdMSzF49BtlTNitZWyMgjtMO4iptVy6ZYMp/nJLEkixOUqMlGcHA4J8tSjDMIGQxJmqYomSCFp61Dw0HXdsRRjLWGstrw5s3XdKbh2fMnPHnylNE4wtqW8eiIJIlxNFT1ezbFiqqtqaoOJ0KdxjmDo8ZJRWMK6q6gNRZhJcvbG7w1dG3Ho6PH3NxcYxzoVHO7OmNVnDF/ekAy8iSZpli3ONPivSNOI5xrMQ5ub255/Djh6Hi+ba+OYgEiQCABAyd03RiDF5bNZkXbtVxc3/Dm9VfoSPP45AQvRTDEeLROcD5QdDsnOXl8wGx6QJrmZOkI07WMspRIK66un7Fa3yKE5Oz8giTNGdspSaLxBBjl8PCgx+0NZVlRFgVJkjKbzgDHcrVgcXtDWRakaUqcJlhnGU1ianuD1IbRJMIzBtGyWVusldRVS1WV3NzcMpv/AlxKnmRk2YjVak3ZbHjx8SnnZ5fUzRLXFmhSrIEoTnAYHDt4ZaDc11ptOcriOGE0yjk6OmB+dMDR0SGTyRQh6LMKSJOUfJQTRQFWooeP/L5TkC7Qog8RP3aHNohd1L6zsx/SVexwfvWBLd036Pcdwf0aaYDtxQdO4P5+hr+llFhn7tQYrDGYruO7LN+7prD3wq4Y4n2Pue1SmX1h7P3P3+8bFj1odz9LCCd318NtnYkPHQaDIR/Ete97zfvHL0SgxVDs2FCF9P2NpdFK9dij6B3Drr027Ct0KgziQQE60nf2sc0EHoTOQqQhhQSpCPMCGmdbRumMqvMILD/8+Ofc3G64ulrSVJJJfkg+TjG2wouOKJWMJylZLvGiC5mP9AglSIhpW6gqi4xG5JOU1bLk9nZDVbdhVkAo8ixjnE3YVBukEuTThNlRFuCqTBLlGmc9pim4WLxmdVFTNGt0klB2Bc1tTVOXRFHogJrPjkizlHSUMz6ebx2WiCI2Vxs6a9CxRidBOev581ecHD/mP/7Z/0Y2ztGpxPqW8SxlNMpwzqN1TJxkJB6atsMawc31giyZ9xKnbbiFPazXKy4uz4ljRVFkGNNijEKpjCiaYozFiYbb1S2VXRPnjsV6w8mjMVHc4nxDNorxhQNhSbOU49kzUpXRVE9oypKmblgsVizXq9CpYz1O1nz0g8ckkxYZd0znM+pqjTNhiLFrK8bpnESnpElKnGh0BPgOIUHrwAckVdDSiOKU65sr2jYwsFosSZ7w6uOPsK5juVpxeX2N1JqXL1+idMdiuUTIGKkj8nyKEBpQ5PmY2ewgiEX5IP8ZJTGP/eOAOUvFxcUlzlviRNN1FmPaPvuS6DhCyZbWNjgb1AHpn93JdBImi20QWpJKUxRrOlEgpQXpmE5HaC1x7gqoUTLCWkkcazbLhtFkAs5ijebR8SHlukMcaH7wo5f89ldfU3ctkRRk8ZjZfEY2ipFKEsdxL0eak+c50+kM7wVKKrRWRHHCbD4lSuLtcxkQhf65lQIdRSRphukMiVJEWvVF68CnZLFI0dPdeEuAyweb0zd77KEA+wZ7377tN6Lsc6HtzzMM+ww270N67y304+4G2PuQ+XAcW9hcBGbpfccyDPr9p5bvxZK6lzfBnoe7b/zCanfTlvtFkeH18Mu9CzB8bu/kh59DWrZ/oXfv3SXWG9LAnYEOWUmY9GQ7dCb7SCMoqd1VhRucyfDzzpcme/ps0Q+hCBFuvP7e82J344Rjcn1o4YJjEKGeIUVEZzSKnDRJUFqSPXnKowPH0fwZV9fnGFdhfAmqpXMFTbfGlGvSTG21aJXQwTlrRz6OkNMcbyVHj08YnV3z7s0lddmAcJRVidIC61vQkE8jJgcprWmRsSOfBZqKYlMhMsXtYgNZE9r9ANuGWkBlPON8QpRKsknMZJYHw9erwG2KksVijZU2yKJKCdITJTJQTyQOp2oa59FJRCRikJqiCOpkaZqjlOLm+oKLizOKdc3zZ1PqqkGLGq2SHhI0zA+nRJHqZzksEDGdPEbKlLrd8OvPfsXZ1Zc0bknjb9B5RT7P0HGDjLpAo+A8VVP1A3kpkc4Y51P8gUUKwcXFJV3rQcFvv/or4pFCjwRoQ92WXFxegh8TqQytIrx3aAmjcUakE4ypKSuL1grnY5q2Q0cKUzdAR92UON/Qmiq0PiuPd5758Yw/nPxRgAA7w3q9YVNWvZC9oLMG0xrSbIzQitnhAUmWkyYxbatp6govPGmWY12Hx/KTn/2E+cGc1WpD3VRIrUmzhLYJk8Jt2/XtkoEvy3tP21a9rkfB7GCCNR3GdFgX6Ng7a7CuoShKsjhinI85PdG8/vo9s/EBk9mc9WaFaSJcmrAplhxMM44OnzCdHPHlF1/Sdh1fffkmtLE7zS/+4T/CGhiNY9I8YTabkcQpiJ5WXIS629DFE4aNB/h56JIM5JiDzVIyItIRgmCUtZI4F2oyxgz03HY7wOq2z3UQCmIPvnoo4N2P0u/Yunu/72ymuPNzv6tyi2B8YNvEnZrEHeZWKQKc229v0Pr+Lst3dwp+v+tmOPHtGd2FiLb/9wbzDoIyDJH57bCGv3dBhovm7hVetsteJnI3i3gIO9vBPYGqYsdLtIWIlOqzhq3vv+NMdjUCtljg/rkORXbY3Rzhpf0uAr/X7+RABG0G74M3lyIi0RO0TDGtBaHIk4QXTyeM8gOub9+z3FzgZIVQDuNruq7Ayw6lIUkDY6dtHdYPQ3ahUKak4vTpMUmsubm6oam6QJXhOoQGGXu8MnhtUNqHAqgOpGqjKCI9THFyjjUKTESic9arDW++eodtWjqpqNyKpNPENiGRgTpaCkEaZSgR0XYSKWO0jtGRZL1Z8sUXvw3toK3BoUhVTFM3mDYCn9G2gqI0nDw54piI69sNWaqIVEKxLmkrgTUeHWmKzZp8FARo8lGGisJk69XNFdlozPuL17y9+D2NvaW2C0p7QzJRNKwR0uGdRfrQrrupWtJ8wnR2RCQymqrEWkeWpRydnCJVxM3qJuD2PsPIDVEakcgZbS2ZzZ/yaPoJ4+wA21purq44efSKJEl7OoxAsugsKBnTdRYpQUUCZT1SO3TsadoK68PkvjWOKIkQSA6ODvnhT37C27fvOHt/1lNaxJSLNZ1r0XFoTU7ymLqu8ViaLrSmzmcT0iTtJVRbnr94zqYoWa7W3NzcslotAUmWjpiMpxjTsLi9Qqkc5zpM1/L555+xKVYcHEzRWpFlCd6H2oUXOcWypGk868UC2xoinZHEY7Jkxmz0CCVysnhKJFLGWYrpNO/eXjGb50RxxPGTY1588oyulPz4B3+P//r/+N/yq7/8G/7mt3/FVI6ZHR7QdC060njrUFJx/v6czhjyLMdZQ9MYoijl6dNnZFm2RQMCO8KgiriDm6WQSOlwTiGl6+sBri+8q74YDdZZwGylEAYFuJ1tpN/uNnq+U5vYh7f3kZSHWF7vdCyJnb3dt2/7juBOgOy5M6sllSLS383cf39CvO1BBazded9rkO4b9pBq9XDdVqwahvoBg7/o5wQ+rDl477dB9t3q/oep1W6/7K0ntjDPQH09FJb3HcLOQw+xxd1t7JzN/fpEX9hml1J+uM69ZfA63uOxhJ4xg5ChWOYAXIySCiV14JH3noPpScDEzyzr4pLWVOAjEEmYNhYC52QvASlQUbLFWYVQOBM6McaTiDw/wRnP4nrJqlhhMSSZJs41TnSgQgtvaxocniiLqMsF42NBmoyJ1Zg8nrBZjTk8GXF5ccPyZkXR3uCLDplIkjwj63KkHDMbH5Bfzyhu1igZI1D9fAg8fnLCzeKCui5RUjDJxrjWsl6bUKaXEutrHj1RPH/5gjiO2VxtsC00VQVJRFVViCZQZcwOJiRpzGQ2oawLFusFZV2TpBEdFcnEcHV+hZMVXjmiTLEpC4TMmU6mJHHOxlRonZCmE6I4D4psozl1XVJ3gQRwNjskGef4rOM3v1+zrC6p6pY8z5AiJs8POTl6Qb0wjOOM509fhAl5LcJk+TAzI6EqN2w2FUkSI4SlbIogRiNt0C82wTlHWmM6g3OexSrIe8ZJwumzp732Rs7hpkLrlHwc09mauimo6oJisw5CQUpSNpJ5PEPrNAgrdQ2jyQgv4PLykslk3NcjRkQ6IdKCpi7I84yu66i9Y71Z8vnnv2U6G/P48SMODw8QQoSCbicoiwtMq1gvG7qmJUsUaTKhaxQX50EaNtKOJE6JVYL0wTG+P7ugbSs60/Anv/gTnj3+AaePPsY2ih/8+FNW1QJrO96+e8vp6Sm3q1ucs5RFye9+/zuMMYxGI+qqBK958exjnj9/sWV7DTBzGGK7D1fvEAFPMBtDUVn2M1EW711ga0ZgtyR0g7H2W7uyDysNdDzDvu7UCe4Fw/uv7//bIhd+50zuoy0fQFd7Wcy32qQHlu8+0azUPeMMENqfdgNeu5r8gD3eL47cT512Vfu70f79QvGum4ke39t9CXLP6YQUbucEhmLyPoTkhb/TdrrtgJISNRDH7cFHdwrezm9ptC22X1d96wUfjl2Ifup7O/cQhEeEFHTGIUSEIAan++hRoZXAS8con/PRyx9yeZ1zdg0Yj/SKulvj8ZRFh9IyUGM4gXND1NKiZJhebhuDwjPOc5Sc0LKhc5LxLCVKJNa1RHGMdb2z11FwDGOPMx21uaFpCtblDc4IjDI8eTnj6NGEtnZ0raPq1izX10ynM7I0ReuYNA4Fz6ppscYxnmR4FzIa48D6iMVtRYIhUWOur9+zuF0wP5hz+OiQm5tralNiuq53WIbXX3+B1jkvXr3i8PCAfJQynuQgQUaCN2/f0NoVOrUs25Z8EnOx/ByZNDRtRZxKdJJh2pabK0OicwSeqmiZ5o+JowzQXF1fYoxHa5jM5nTGUbeGKEt59OiUi+UJ1cUFXnm6TuBNwsH0KS9OP8XOJbY2QathoKqgxfmAz2sdEScxMxUhlaAoF/zqV39OkocOrjTPA3YtFFkyAiVp646qrdBxTKTD55ywNKZiOh+FmQ3RcXnznsvrc5RSPVOsIU4iyibQm8RJTJxGyGho42w5OJgTxwlJnBPpNJgH33Fy8ogsS9lsNjRtxY9/+CnPnj7h7bvX1HXF9bUjTROMsSTphPn0MY+OZxTrJbfXC87eXlBsLNeXF0wmc5Ik4caXTMZHXK1v+fsvfk5tlpiqwgsYT0YcP3rCZDzGSUecJcTZmP/+f/gf+OKL3/Gv//W/5svXX1FVJXVdcnt7izEB9+9WYZJ5MpoxGuXbeuPwLGsdIfR+C2gw4krIO8/qEN/FscR7u5UBldKglcbpnfCXMeZOpL9DEvabYQa7eRdeug+r7/+7i37sB+S7ZZ+GYx8pGYZxt45FiDsIx7ct36PQHKbk9l4JN/YwnEWAhHpfsT2gXfU+HJgnYHFSCLwI3EL7NYIh0wgn57ZNnV4MXT1+2zEg94rbW0yvbx3V2zqBvHPRhBB9G6q68/eA2wklET0J1zAnwV4q1scG22xh8AUfdgR8mNXgHaLvRBiupUOADR0koAIPvvIggqax9x6cAK+I5ZjTxx/jnOf1+xZcRyQyrK3pbItHIb1DOIuzGtvPXUgFUnhU5Oi6GitgdjTCJ0csV0t0JFEikNn52uM8dNagrMU5Q9w/AL6vKxlXI1DoPPT/t96g0ow4zbCtojMNna2QOlBd53lKtJZclyWr5ZrV6prTp49J4ghhHBpBURturjZ429IaT9VV6MZzFGVU5pblxTmr6wVm3YGTZKMJh0cZhhveXJ1xcDhlUW8CfNSOuFy8Rqc13q8QyrK47pBRhZSO1aYm0TGKOGgZL5csb9aowzGuC3rKo3zCdDrh8KjmL//il0Sx4uTkmMOjI5yVNDcdKMvHz/6Arqm5XZ5hOsPR/BkvHv+EOJojZMRtcU1tBF++ec+yWPLoeEKSCPAOpTra1jKeTEAYLhfv+avf/ZJsHAr9wUAnpMmIUTZBS01VdiwXBePRhIP5I+bTOd6qni+rIR8prG2oyiXr9Zq6rhDCI6UlHyWcPDqhs56uDPKcxkDXQt21tLYmkhInaiwSZ6CuNrRdi4wUk/kc4x3GW8TVGUftMUkc8dWXXxNryTg7QKCxccc4HzGbTHj57FM++ahmtarQSvLkyWOSJOU//vmf86tf/SUozx/8/GeMxnOqukTS8uTJI5TWICxRkpBGc7zVWNdycHjIP/zFP+Lf/pt/Q10v0TplnM/wBLhns9lQFRXSRrx7/Y6D2RFSSI6Oj0PNiD6a3z6vEtFj8IL9ADbMEivZ858FPuKAingPmuAMhOjV+MxOO5rQCBPaDO/aiR38PtRG73cwuT7DCPZhQDsCXWT4/Lav0/ezYw9mDVtEG4/HPtAa+03L9xheCwcEd4vBdyZ6973bPmYmBFLsJo+993gVhrq2juZO1b7/15902NwuJQrKTGLvLP3WAUj9oYTmfsQv++Ly/utbGEmELz9kDUP0vxs0Hy7B4BQkoSV3P5u4W1y/dw2d29ZPhnqJp+9A8ASnIQAfYIphxF4LCSIGIiDmZP6Csih5f17SmBVoi48dnbF4E7ifIh1aZdEJIlagHUIGx2BFRet8mIvQoHVw6c70fdNKbhXGlJRhEE5FGKCq635y1OM6B1biWoXyDiU8votwQuCFxfuO29sVtzfnnL1/y83ikqosENpzfX7LeDxCKoGvDSeHpxzOXtA0jihSHG0mWLdE5x2r8gxjLEW9plgsefH8JU+ezqjbBbflmsbULFpBmqckcUZcpVyvLhkLSKIW+vY8IeHyfEGxNszHR0ySOZtmzaOjQ6SQ1BuDcAlHsxNwgrIoOD4+5MnTE969/5qiSjngIDCptpZROmE+zZAfKX7z21/RyJYfvvw5h9PnWKPxBt6dXzMZTzh8fMrv/vIvMOYJaRp6/fMs5+z8nCdPn9B0a75+9zuOnoxxuqGjphY1m3WLWEkkCtt5sIqqMkgR8ejRU378o58RM8ZbyaZYUVZrolgTRRFZpum6oKfsZc35zZKqfcRiOePps6c0taaqBG2TYK1CRNDZAqkt3im6xrEu1jTths5bnBdIFZNPptx+/lv+7Z/+Ox4dHTEfH5JFUyKR0ZmGti65uGh49epjDueP+fjjk5BdC4OOHFmWUzUF62rB8eNj4iTh5OQJp8+e8NWbv0ZrgdIS5w1111LXBcpnZJkjybKepvs5P/rRT5jPZpjO9EVmyft3b/lf/+W/ZHmz4pobfvf574iTlNFkRh71E/rWbANT2RdkceE530bifXRN/wwqKftidGgW8c6FzwswwgcH0DeQCCTGhoK0CHKQffC7Qzt2Ef5ASXEXeh+6k+423uwC0wGJkWIYAdhBS0KIYTxsayND9vBwZ+b95XtNNA87vt/lcx8b23cY20Xc768N8Ak9+dPuQt3H1IZU7j6UtLsYgxMInUR3+YgG+GjY/n4t4aF/w/TxfSjr/t/sHdK+Q9gfU79/zawL/CP7jtO6HbS0vVS7Fi+E66OEbcYCsc44nD9msbigqJbhxotc6LV3Fmct3obp1UhJrA3dI50NA0YCcKJDykAEpiQEecK+T9tZhPdIJFJAZyxZHCCfpmm22LbrHN5KNCk4i5CWLJ0QqwyBx5qOstjw/u1bLs/O6VwZHkIRDC7eEScRkZrw9PEnKDVGqRZjSsbjhKKGulltBd+F9qg0ohOGdXOLEwY9alDSYn2HFx1eeqq64+b2hqJyHBwlGOMoy5au9VxfG/LkgGc/+EOU9CxvfoO1NXVVUVeOZ6c/4PT5M9ZFQVnDfKb50U9+StXWgECqFIvk8OiQg/mUZCSZzj/l8vKSzbogT6Z4K/u2Yzh5fMLTp09I4pwXpye01QbTdigJbbXmi9/9BhVZxrOE29sLlApZWV21dF1DuSmwbcfR4TFda3Cdp6k7lssVy+Ulq/UFzx5/xMmjU7J0RGc9TRm6a7SOQMLN4py6vSbJHdfLBavSselmWKsYjZ4wSl6AmCFVjPOBMiJSmihWvUZDAkLQtkHgKs9G/P0//ge0Tctnv/0tUmqiKOH29pZsrJHac3bxlo8+ecXhowMmo0MECk8LokXriD/8oz/iyfNTdBwG7kajMfk44u37iPX6mtEkI1KKql6iZULXGdI0wxmL7YKoz3gcJtpD1iW5vr7i8cljfvGLf8T/8i/+XyxXC2abKXVdUVUlURyh4yh8j1Jum1WUkAwDyoJdkVYIMD7Ut/B37Z4TAi0lQimE0iAl0unw7HXBWTj8Fl4X/i6cfr/QvA8bBRvCdl5qZ2N2WcV2/T37Mtjc+5D9EIz/LcBH314b2F/voc990zoDfIP/sJMoRNIf0sOGixaM6mD0o0GpSe2mj/f/3d/u4Cg+wOP2jvObznE/mrh/Tt/0GaAvWAWHtr25tjfG3S4t0WcgbJlNLd6FThu8YpTOODo4pTU16/IC23R41X9GukALLfsuIivxLnSbSGURiUBJF/rWe3pu5+xeghb0C5zpmwW8xxpLXTWYtsV7iUSioggvBcpHuBaiKOJgNkeSYLqO8/fnVE0TNJfbDh1JPCHzkErQVoZyY3j69BRvI5oupN/WGTpTY2yDd1XQ9LWetnVsmpbHkSLKNa2tcabE0eCwmE7QNjXCjZiMc1arWz6/XoAHazxV7YmjnL/z9/4+L5694te//nNAMZ/NqGKN7SpmszlZltO0TdAXVjGj0YgffPIjFosl3ku00mw2G1arS16+eko2Svijn/8xf/3Xn7Fe1RzPw/xLXVbMpyMmoxSsZ5LGVHWJ9R3les3b5ZKuLbm6PEPFh1jb0nZVGKwrWq6urqnLkqqswSimkzHWtLRthVaOplny+WeXfP31Z3z06lM+/fQnpPGUpg7CQ23bkWaal6+e8evfvObRbIaKN8R5zbJ+T1V2rOvHzMc1efIK7Jg4inrdghZExXSaMhrFFGVDXXdBTU96ojjmH/3iH/PRy495/eXXNFWHcwZtOqS21N2Gq9sLXpiK1eaWOMoZjRMQQXktSpIgu5qmvHoVOrOUtBwdHfGbz19TtxvGo448T0niFmM8v/mbL3n65Anz+RylIjarTf9cKbIs5euvX3N29p6PXr3ik09+wNXFDU3bUpWh2B4nCQlhmEzqnpLfeYTez+4Hqxt+DMzE+3XP/Q7DrY1RAU7qug59x451CKFgzykM+9qfG9ifbh6QjBC83p2s3lmoHTrzkE0eft+3c/q/dPfRviG/W3C+6wT2U6RtcYHB0+2RRt3Zdlh3awgZviDXQzd3sxEhBLLPDkKWoHZ02XtGXykN+LsZCx86gg8KOnuObP+zDzm4+9fk/vr7S+hQGuon26Tvzv6H18L2hrH+PpWVAbKyTpHFYx4fvyBNY75+p7hcf4V3Fh1FCN/29BuOuqp2+CKBd8la30ti9mnl1iFL/BBN9H3YzlkiKcLUct2BE2ipkUqH45IKQUJnwRpLVVckOtBthAKg22o54EHJ0E8uHDSVYTw55uT4Jc5FtG2LdQ2NKajags42CB26N1arguXC8fh4RpQlrDZrNuU1Ojbko9BS3LYG23jKTc3TJx9zNJvz+s3XrNYrkjhiMsnROuHs/Cs2mytubi9ZLS7QakbTtIzHOca0LJY3pDHEWqK1oGka0jTjYB6unbUOIR2//PM/w7qKjz/5BGPh8clzsIrlckOe5sRakWYRuBaJ5uTogKZTRMkBF1fvKZs1T59/gpfw5Ze/Y1OuELHn4vycq+sNdVERac04m1GuG4pVhdZh+Gt+MEfpoKtxu1zy2e9/xfvzN/zsp3+PT179lLZpwIKxktE45eTkiNX6guPHEhUXwJpEdxTrFe3C8PJ0TBrHtFWDICXSgrYzWBuTJCOm0wnjkaDtOpq2oakrnHPMZnMu02s2qxvWmwWFabB+TWdL1sUNVVMg0oym7LA+wdNrLLuI09OnTKYZWiWUZU1nWvJ8jPeCy6vL/jrnSJEjXM7jk2PiKKYyFWfvz/nss8+ItObTT3/I1dUVIPgH/+Af8pOf/IR/8ot/wr/+V/8b/+J//hd88eWXODxN23F4dMhoNEZLFZChexH78PwND81QfwgMBAL83Y7HoY4g+y6lXdAqcS6gF8bsaLv3g+t9BzNE8wPqMdiDhzqN7iAWbph9ursMx3C/cP1dlv+sieaHDOr9ExVi39jv8LTwurw7mOYGSOW+HN7gFO7CVkpJVBxt6wb72JvsOYx2rWG7z+9794fPgTuZxf06wf1soEextjfWsI/7X97wmfs33/4x7V/f7c9+cEjSK8U5gXUGazwq0kRyxHzyhNt8yfXyHOODwAiqr4sgemzT9tFMKJaFAngQkfeuh6VEuBWEF4BEeY91Dmc8Rjq61iCQxDrFEyJoJRTW+L51VlA1Faa+YpJ56tKQxKEbpmst3g71kwgtZCiMojg+OEUQhcKaDy26pmtC7SECugBfCa84nI+YjEe0dZCLlCJBmIiudCSppt0scK2lXrectV+Rj8bECqbjmHycoGKJMRVFtSZOHzE7iIj0BI8gihJM51ksr/iz//C/8+joFR+9/Alaa9JkTF1XpGkOIuiEN02BkC4MeTUGJRKSaMxoNuH68oaubTl+csx4lFCXG4RVTKcTysZydfua84v3xKmidQ2vX79hXS2RkWaxvOFmcUtbGdIkZZyNwQmausZaQ5anGAFl0ZBkURBKGk1omg5jGn7/+V9TFyVPHr9gNjmkrhuqpmY+P2b59QU3N2tmsgS1QsUWEXvq+g1F8zVxnKDjFI+l7QyCmLousdYymR6Q5xOUmtF2Hbc31zR1ze9+9zu+/PILJDHGGlY3F+ikQSjDzeKSz3//N/zkh3+ClKElN81G4AVa5SSxpm0rWuuQMnAvNbUhjhOKoiCOIoS8oSoFk+wRKtKUZaDcuLi44E//7Z+SZyOchdV6yR/90c/5xS/+MVGkiJTmD3/+d/jzX/5Hzi/OWG/W3N7e8uzZM54+fcpkPOszjIwwlbaX9e8Fl1tbtGfLBHenk4NNCZriQ6A1vG6MQiu/5aEaYJx9m7JvbwZRscFe7ILsD9lX+18eNPaDTsT97qbvsvxn1xSG1x7CxoZj3G/F3A2ACLy3PRYWxC9kWHnPm4Wswt05iUBru8sK7mL/g9EbCMOGRYihqLwbKBmmHIcIOhzTXc/8kLPbbfPDbOdOwZ1vgNH6GwbvEV5sOZ/C6YfCcB+MMEw/O2uwLkA5prVY40EodKRx3lG1HYke8+jgGcvinLJZhCEnITBtr7GAIlYxzhu6tk9ZXRhs805gehGiMITj9mYuBMJKrLEINFpEODEIloAndIc5OxTPDKvNgq4SxFHOeARx5LDGYzqP8oKuaYIwfNXx+Mkjnj55hooi2rZBSUfbdXRdiKxxDg8oFNPxmCSKUYKgCT09IEtzvLfcXF+weL9ES40zHYmXiLbmerMhGgkeHU84OJxxvbymrgsAlOyItUZNxlhjqOsKjyNKgjOuuwW36/dUTckoO2A8PiAf59guzA8URY3AYUxHXTWM8jxsT0ZkaYoSBHK7OMK0mjQb07QFi8tb3py95c3F28C/01UYOoy3LG9vWa5vQzcMjskoR7hwbz45eYFEcnl1SdsZqqIhbRxRFFqibReem6pa8+UXf0OxXvLjH/0UpRVxHCGIeHz8EV+8/nO8sESZJc5bkB7LiuvF7xBESDcmUVPydI5tK+JoTFHeIlS4P7JsQpplYRJ6uWSxuOX2doEzgqpeEU8q0rFG6Yiuazg7e48zv+aTj3/M8fEB0+mYOEpwNqKpK0BRVy1SSqqqpe1aQGB6iU9rLWgX9LJFh/Sam6sr2qbGOcubN69RSvHsxVOePnvGpig4OJyho4hXH3/E//h/+7/yP/0//ifevXtHsVmzWa0wnWGUj5hMJ5w+OSVJU4RQePquyD7as96Fdm4p++Iv2yRicBCDTQl0GB4pNXG8M+qhMzM8M0P76n6GMNgXvWe3jO22jmh/O70luWOPQ/n5Xq1zzxZvbc/fRqZw30s95HXuFlj3eYhCdCr3jKBgzwEMDa1+ELXYdyZyWyPQWhNFOlAl3HMIQoTJ3W0WgNhG8fsX7L5To2+RHTz8/joPQWZ3PvtAAfzbL77Y9hr7nqZ32MSQdQzZjbOOrmto66r/2VCsK+q6Q6CJ0xilBXWzoTYVgoQsnvcc6g3W12gRh/NQoXimZYxSDuhwtsO0FqU0Wsa0xmwJw5xrQzSnI5SO8MjeUWiMD3We4AgcwgusDbMbSRohHGRRxHg0wbSGTbFiU2xYbzY8Oj5AxTHL5S3z+SE/+vRnJHEQ8NEqUIt7L6kqQaSSUMAThPvBQ7leMc1TlFXM0mPSZAZeYJOUy9d/w7PTKS8/OiFPFVc3Z1wtzzl4MmU0zrm6vg6dWSJGKkW56dCTEd47irJAKZgfToLKmGvprGK5Omf+8oCDgwkH8yOUimiaEiEkSkuSJCaOYkzn8S5QmjsTZDClcEgVaBhsnAT5Tzo+++K3LIoLjh4f4nF0zvXcOg5rOnCeSAhOnpwgvOL2as0nr37MyxefEsmYz/Tn/O73n4WgoPZ0nSNPM6gtnpZ8luCx3F694VfNhucvPmI2D5DJweyUi4v3rG9fk3YxHouMLDoS5BNB3Z1TF5c0pSdPDvnk5c/o2jXGKRZLg+uzTt1UFOuCOI74u3/373I4O+LdmzMWq4ROXuNd06/rOD8/53e/PadYN5yePgqBjrOAClToSlFXhrZt6dqWpqtxrgMcRbHm+PgVeTIm0Tk3F1f8xz/7Jf/+3/0Zi8WS5e2a2cGcN29e83/+v/x3JElCVVVw62E+J8sznj59ynw+41d/+ReMxxO+alo2mw2j0Yijo2O0Ujx+8pg4zmBAG/aYCEI5wfdBZN/6vlWNHCBvGYIpwnCq7OlvhjbuwSns26sBKvLe3ykAhyxiCK7lln1h3870hxTW8Wx54PZt131b9l2zBPjPqCnc3/m+F9oZw3vEdHtc5fuf3cJJwwUf4CJ/d7+72oEOFXkVqDGGwbEdfCS3/cThMg77+NAhPHQeD/39n7wmD2RO93/fvwx+79hEf2PtGo/7H/31sJ1luViwuL1is16zWZcUmwbnQlFX6aDa1bmaTjaMZwnG+mBkI42K4zDfYQnRYSKIlEDICCkdTvn+Cnki6RCu7adN+24p6zCN7QnSegzVqQBJOdczSYYsRuiUKM7QUpJIGM8iulpw/v4a71tevnrGs2envHv9hjTN+eTjTxmPZjjf0VmLd4ZICw5OHnN4OOfy6ozl+gbjDF1Xh6E0JKZ1TPIZ42yOEiOsgUnm+NEnP+PRo4ybq9dslCFNNQezEUfzEUk24t3bc66vGpRO0ToDmVNXDi9axuOE+cGYsmqIIokRDudbynqJ9x2T6ZjJdEJTd8RxTNcFsrokTugay2KxJtJT0jTUJAQOpfrAxwu61tD5jr/89V/wm9//hnwMy7JhU6x5/vwjHj2a0bUZF+++hKbh8cGcn3z8CZtlzeUX53z1m8+Ju4iPPvqUl8en3L4/Y7m6AQsq1igFI52idMQ4TfB0GGcp1wturi6IVBQo4aXi6OAJn33xGikT8lGMo2Y6O2I8nhBHI14X76ltx/XZOVGimY9OmU+esl43NO2Gtp/qdtZTFhWz2YR/+s/+TygRc37+jr/8m3/L7776yyCferVAyyl/8vf/K/7o53+fJI5x1mJ84HOSMmgaPDk9oSjWoApWFxXILkxltw3WdIhMEccZo5Hl6GhOmsV0ly1pHlPXFf/8n/9zPv74o6A85w1lGTrbIh3RNBXrzQqpBHVVBbZQ0zEajRAIbm5ueHTyqJ8rEPgtnBue0uFZvvNsA/SKjUAvY7o/3Kv6TCDQ2OBgqKdKKbdiQAMqMQzGfVg/cGGGqncqOyPxoW3atqLes3P/Oct3dgr3l/s7/qaD2F5cH+Ag4QN+OMAvwfh0DPMK+5G/lIIo0ltRjKGYLITvncIOFhp+h7sGeT+12r9o+1/0HUdx75weqg8Mi7U7LYeH3v8g2+j/+7ZrN6SLrudDb5qGi4sLzs/eUZU1tgucOQiJkD505mA4On3Ek0cvcaLF2po40VRNgbGGxlQIaTBtTdfVaC23rikM/IVaghIa7w3eOpz3dJ3dPSyEG9xYR2ct1nXBsTiL8MFgA0in2LQlm3cLRukhOvHEKejIk6aa8XTEo8ePePbiWcgyOo9GIbVGx31XE5pIpngjGY/GrE3HenNDlqZ4K5jNDknTHEFCKwxOOJ4+e8z52RdcX1+R5RFxPCGOEpTSpHHC09NnnJ1/hrWW6TQnjmPqZkOceI6Pp8SJpO0U3oVCcttWZOmc6XRKFGkinbDpGqSMsLZGIJkfHKGjBNPZfqI2FObjWDHKE9I0xTmo646iWVJWC4wpWC4LqmZJWa5R3jAZTdFSENuG8STn9OiQuYq4uTnHrw2NW3CjXvODk2ccZxmfPnnCe9lxvXiPcILxZMqTp8+ZzkdhBuTmgsVqhYxjFpc3xCohjhRR5JmMErRQtJWnqxN0kjCfvCSOxnzy8Scs1zdsqhuikeSrd3/Fe97z448FcTzFuBLvDMYKpFc0bYnORqRpRKRiXr16weGjjMeP5/ybP/1XfPrxM/7kj/8rHh29CvKXcRw0KdIEL6Dr2gB/KYl1JUI1CFWhdIOOHJtNwbuzd3g7o8BBbfjJj36I8PD//Bf/M1JG/OynP+O/+ef/LGgt9C2gKtJsig1ZmlFVFf/sn/5TlJT8xX/8JV1TI72nFpK6KkMW3jYoHW8zhB4lCkQ+W1zef2CK78IyioFJ9e5zLftjuzt5vN99tKW3Hobf9uoJ9+uYg53bbtvedSTf5gz+VuCjhwqwHx74QDE7HES/ngiRk3Me4UM24Pe2PSwDTDS0Y0XRvYKyCvwkHrZ1hcHJ3Deuu0j+w8xgv1C0LZCHDz5osL95+9/y3gPLd/lihujBGBskJsuC5XJBU9dhiMn4LQTlsSBD4XGUzZjORgjliJOYotxQtxXG13haquaWzhboKMhF1lXobonjiLqp8KZD+kHZztLWYe5ByNBlYR14ocM1RfbMmRZJUPAKN7VFeNcPWRXEeoKKBGW14P0ZSBkxmmToSBDrBL9x4HTQdvCOYrNERxotE05PnjI/HHF5LbHvV3jf4sUYhKY1LVEkQLZ4WTI5nPG7L29BCx6fPiGJNOuyxXeSSMU8Op7z4sWMTdmQZDVtuybJJKdPj5lOU4qy7K+7J89z8nzKweyU6XSOEBqtE6SIsV4QxykSx3Q8IVIjmqZvN7YWIQI9SZpGRFrjTOC1GscZcQxtu6aoLnC+QgnH8uod5c0lpmkxdUmcRWxsx81nX7K+bZgIxWiU4TcFV198xc//7h8y+ugZuSoZ6Q0qgtNnRxw9PkRpTecMTTKmkt22DbPcrHn7tqKsJ0znKXmuKSrL6rbm6fMXZPET0iShrFuEskSpAeUxEhaXLV+/+R0fvfoJbWNJkilV1WJai7PgrEFKj9IS3zm0Sjk6eMx/81//txwdPeLw4BlpdBBoW4RCaXpuf4v1jtW6ZLVesFpfY9yS69u3NN0KIWtU5BiNEqSSrBdr6sWadRTz05/+mGyUo1TEH/zsD9FxzHg8JstzICj0aaV4+/otp09POTl5xMcff8yf/vB/5z/8h//AF198gZKStm0QBJGoKDJBataHITbxwLMaWA32sN69oP3bCroB0fgQgt7XXxnmp8BjXeieNGYYurzLyNr/Fn5IwTBDMSz7tNzfB/kYlu8nxzkYz2+pLwxpzr64/V3DuWvJtM5upwp97zyGIbTgCHbQ0X6XUaC82GUVg4hFMOxDDDxoMz+cLQx43p0L5v3WUd13DA9dVLnXpbB/ne5znPsek5RyS8P64Q3HUGDu13ehxXK1XrHeLOlMHdozHVj64jMWsP35GazpcC7c5G1n0TohjgVplNC0S4xLQHXEUcp8MsZ0gbY5CKRbrGnZrJYsVwuqqkQRkSQpMlKhDbFtsVuH3xfD+0EbRBBQD0R/HuMUpmupqgrvFBLN9aLm6ZMXjCcp1jZYIbffUVNVaC0oqg2HRwfkWUKaj6maJXVdkqaasqpRSmONBeHxGFpTYFzBsrhkcpBxdfOGX/11zfOnR8TacXu1wllL52omI8H8YIzFIXVCnKRMJhFCWpzrqKsKpQJ98osXzzg+fE7TblgtW4yRKJmR5glNEzLe2SRHkqKVJY4iokhvodLhnlNKM5vOsB5821AsFvz/efuvJ8nSNL0T+33qKJfhoTOzMkt1VbUY0TMYqOUKEABvlkbe0HizZrRd/k0EL/gv8IK0pdkajdjFri2WC8wMZjCip6u7S6bO0K6O/gQvPvcIj6hqTDcI8liVZUS4OMc9wl/xvM/7PJ2rUNrS9x1KeoKwNPOKXDtcW+NSw1hnzA732NMN63WH6z3rqze48glpGnj/0QF52tC5liwJqNDh2wZvPcMkwxyeslg02D56T3fe03Q3OD/GGNCdwrae2eQI4RW287x8/oKqXpNmMi6ahZ7Z4YiqvKasL6mrwPGhputrRDD0naXuPAGHDz1+oxE0GIzZm42ZzfbRYhhVf/Fx8J4anIsMM4Kj6Upevf6Ktl+QFpa6vcJTI5XE9pYkSdBK8fjJCdkTQ9c0TKdT5qsVw+Hk1nRn67EgtSTLUq4ur7i6vmQ8GoEPTKd7/Kf/6H/JJ599xj/7P/8zDg6OyNKMsqzp2p4k6aOMffxwxg+lijsp29nj5j9C2MwUxHdZQZsPRvzsbz7XMZnEqn4rureNXdvHbllJWmtiY7FBDJwjiIBUURVityv4Pt/nXcbRv+/x29lx7sArbssMUeq26pYysn9uqZTbxEDE/53dbAduAlncGQmg7wd9qTVyw9WVxiCVjuITm2reI9BKweb73UC8HWCrbbIQkUUgNpk9hN1EtplLbKmw7Gby3aAdmRc+Yj+3yUQFEaEWuUlsYZsEH1jrCXHbVsqdrupeFpcyVpoAPuAs1I3nZrWk8Ssqcc14NGA4KFivwTvNulwQhCXNDbqoadwVg2DwASQCZSIjKIiOZXWFUA6dGKyTvH59w97ejOneAUpJDg/2o/nJesnl5TlltabvO04eHXH0eJ/Pf/WzWNEtr+ltTQgtQmo2Lw8RwPaOpu7pGkchDc5CuWohBGzXk2djHj3ep8g1rm/ouujvPJ9foTWUZc26XKJMRQBOsmOC62jbGu8DWmhGeU5wjlRJ3p29xfqOul3AZc3RyZTJ5FNevXpJ010xHc/ABmxZowwcjKc42aMSiUoU0ihs6Li6XvD23TUiKGzjMSbl+Ytfcn75hhA0zqU0/pzjow/J9D6mGNE0DbZXjMYjOlshlEKbrdnThnYoJSpIhILl+ZpXv3yFrBXTbA/hOrQC2Tv65Roz7xgPBAeTlFGiyVONtS1F0eFSiTIFJhVcnf+CDz79hEwWWHnA9WLByckpw3xIW5bcXF7iekez7siEYNU7hFRMBmOOj6ccn0x4/eYbmlCRJgJsiwxr5ldX9L6kSBR1UIRE4lxLUHOyMayaL4GE5y/fcnVWk5kDlBwwHMwAj0kEXnhMANEr8sEURIFOcqT2EV+XFtDUdU3btiAt6/U76vYVPXP6tga1pG8dV9cVWXYENsX3jpBZgk5IioKm7zFZRlbkOAImTTm7POMQz6DIaHzPX/3ln8duNnhGoylCKiyK8eEh//v/4r/g9PiUm+sFf/w//wlSvGD/YI/hMGc6nSJ19B5wQbL1ktDaEIKO2D3AZri7Df+xQIxB3m9rzFtkhDhPlWoDSUWIaBtLtE5wrsd7H8kdVoCE4AJe+FsWlhAC9zDgh7BZNtwQdQiRtOO3sTbGvZ1pyN96/MZJYbsN9xAX6/v+O23KrZDcLn3qdqHD3+P0SxVXxaXWqE0LpbYuaGLjk6zkNimD2Kx/b27fTuBvz8MmgbMZbgtxW7V93/HdVk987/chhFt3ud2f380htrX+/YC/+zzb7P4QJ7yj6m6uX0Y2g/cdOoHBOMGbAXmekGYSMywgCCZO40XHIE+Q0uBlSe8jWyQ1E4Lym2G05WZ5TZYZhITlzZyuUjx7+jGJGcY/Uqspu56+F+TFkMnehP39KR9+9BQzknz79hfobIjQJU3bIURKCDa2vCHuKlhL/GMOsVgY6pTJZEiwUK06xoM98lQTXIdUKb5vuZmfUTdrtIl6N+9/+BipFNZDkhrKxtO2Hag4+3jy+DHDYspiVaKkpBiMUYnFhRKk4+TJAe9/eMy7N68obxZ0fUPvJdIKqt7jRc/kYIwIgqZqWdcdXReYjEc4ZzfSAgbr1yxWa5yHLJtgsiesq3OUTsmzEb1t+fqbd/z0D/YREpQWkTbpesbDCVk6wLYObRLOXr/l//LP/k+8ff0lQrSIoSTR8e+/mZf0y4ZpojjKE4rgGYRA4vvo/mUCwUBSKPJhTjLK6PsSXeQUg4J1UyNDYDoqWNZrzlZzhsWIfG+MSQrerUpeXZ7zwbNnHO89IpUgvKHv4udifnVN1y9p+hUqDeQqRQmFlgqjLW1XMdk/oK0WLG4aykWgXkkqYfFdTj1xdH2PK9fRLMg7kjRDyARrJaSK6d4YQY/zHX3fRf/qHlbrBW23RCcddTMn9CW9K5kva+raMdrPkcpQ1muquqNIx6RJTpEXTPYmTCdTtE6QUrC3t4d1PfNlw8XFBdpoTk9OGI2GJGmCdSCMQieGn/zeTwguYLTh/OKMn//Nz3n85JTDwxmPHj/iyZMnka4iJUpHerdzDrmhkcdwH/FvsYFv/Eau5haZ2CAZQYRbiQu2ETEI5FYQc5NcvL+bpYpN9yFSEYX2vLvrKnYgbykluDvFB9hQandi0F38uT3733r8Vuyj3ep3hloTZwABAABJREFUNyjuSjbE2+8vZGz/3d3c2z5WySiMt+t4tjXfuT3PBmK4e1fvB/OHM47tcz+8/eHX3/ca/7bW6zuB/nvuE0K454gUwh1G+X3zioeDIiUlJhEUQ83h8RivRuhyhTIBpEULRW8dRoINHpM5lLQEVaFSh0Og0g3vWmqa1pJmBUrD1fUFfSOQokBKRZpmXF/d0LdXIDxae6R29H3HctXzzfOSZXNN8CUhtJTVFdpI+j7qKAUh2VCDQAakgWyU0LeO3nWkMiMAo7zgeH8fJQN916B1YFmuuFm9JUkEj558iNaGuu2o657hcIQPgcvrG0bjCdIEmqqht5bFaonUKfuHx1HuOw+UtUVqQd2tqdqG4SxhMJjRVT3X8xvmyznT2ZQsTajbDnobVSeDJjEKpTxKaoLYCAomBus8bWfRsqNtFyRmjHcV0BFCR1ktqJs1iBD59S6a9RAEmU4INhA6y/pmyZvnL2mbmiyFZtXReUvrPco5ZB8YjVKGSYIKFdIHfAdaSZRWdM4Tuoq2CmSjlKZckBnQWnF4uM9qfsWrco2yPb5Zs6xL8tGMSTZGqCmr5ZpQBTIKpPMkckBXeYL1vH15jko9KnMMxoa2r0EHUGHjTNhzffMGrAYSuq7n5ORDnj76Cc+/uWa9tpEijqIsW3pr49a3DphEUjcNWaYYDdPIWBMOYyTOdSzLjs6tSDJIkCyrmsV6wXy5plwLvl19zc2wZTY9pS47ZtNjjo8ekWZJdOQzW9l8RwjRfe/m5oayqsiLnL29KSZRhODpug4hAkmqIHiUERTDDKkC3z7/mvOLt0wmY2azGZ988gmnp6fsHx4wGIzIi2KD+ft7xTERDY8Be/s53kWSuBPJvwsO92NJTAL3rQmEuFs4FUKAEyg2kjg7rCXvt2zN+/OMXwd3/6aw0r8X+2j3hLtic3dLFt+tkLcXtZ0b3IeL7mSu5QbOiawYeQtXic3gefPyvnNN20AM2wbhfkX/cA6y+7OHs5Lb+Ym8/xzf/3zfTxH7vuTzfcOo3eR3pyoLQnmKsWbsUq7XAdF2oCw6TRDa49oWpEeGDp904FuCnpCPBEmWkWaKtvdEOX5BnmekuWRdxwUvOsvN9SVHB4dMRlMgWi2GENvY1fqKd+dzwrctTjfU3ZK6Xm/+YANKB1rbxwpHqOioJiVWBGQSq/rQCnzXoUPKZDRmkCYE1xG8pbMti/U7qu6GwWSGpSV4MFlOH7poXqwUg9GYul0iNSzXV7y0b5Ai44/+7n/E0dEJr94+53rRMpruEVRNUJb54hxBjzEZ2XDApJggx3E21XX9xldbYZ0jeLExfBco1SA0kZ0lHco5lPJI6UgLT54HoIHQ0PdrPvzoPaxr6TvI0kH0sQie+eU1WYBxXnD27g2f/8W/IZNsGEoCYQO2bdFCUOSGwaigUAHtLZmW5GorCx+dvoKztF1Hb1sG42inKRqJLAZMJmNcWbO+umRgYJhKFosF67bmpn7FopVcny9o1z0fP/0Bk3zMdHBE6L4iBEHTtajUkjhY2R7VQDKIpksoSFNNs25pq5bVzZqmTnlbXyDDO/7BP/hHDIf7DMcTlssSKRRpkuKdJ0sNUoq4QV5ZnJd4H5lv1lpsaBhONe8WK1bNJVVzw3xxzmq94vxiRbWS4Dpsp9EqJTUFRZFQFAkmEQgRvUiKIqNtIkX48uoSpRVHJ4eslyvatqUYQaI1oa6REooiw9kuTuG6SNVGepqmpe+vubq64d27cyaTMScnpzx+8h4nJyfMZjOGw2GcW6itWddOdf9rkIGHiIlA7NgGbGaj3A2a43Nu57IbZ0h3t88grMWJuHD6fUXpw/h0/zp+s+M3TwqbYUd88XfYfdg6EAlQepPZwp163/bCHg55t/9LKeNq9yZjIkXcTN7MDGKAjDDTLTto5w2IWdrfvhHbTuP2jfk1b8bDTuLf+dI31+65P8iJW44B5Hdf4/aXeHsOIAqobA2J7i7tfrLb0GxVwCgRnaj6Cq0BEwiqxYWASjwOFyt76fBdT93PWTc3ZEbjhUVq6G1Hu2nLy3nJurqEkGCUxtoa5zryYkDX9lgXITdjFL213MyvafslQXcI7ej6BiE8iGhub9JkR3bcgxLILaSo4uJNb3sSkXCyf8igGFI1UehuUc1p7BKpA0JKsqzg+PgpbS9omh7rHJ6O5XpJ062xruX08WM+ef93GI8OODh4hNIJx8ceaTxVd07V3OBEA9qDcnQ0NL1lkA+YnEzoWku/grZ1UQXUZFFB1nv6tiErevLEbEQEO5QOJKnBWY/rK/JcoZBo7Xn8+AQjUspVgxAZTdOxN5migqdaXPHu1XP+8sULvvz8c778xS+w1QK6GryMVUvbo4wk8YpxYchkh7AdCIcMCVpst2gtRSIxIVD7nqa6IYSCUEGaKrQec3gw46KsaNc3KCyzcUrT9XS95eJywUAlHE1zjAgUaYHyhnrR4TKBVCDQdPSI3pEIgZAu7q6kCqUNigS5CWaJHjIqDjg4PGEwGpLlCWBJU8mqLWnaEpMkSDXAuRapAh5LWdes1zfUdUnTNORFTj4WtP01QdZYKhpbYl1Dmmu0zFF+zHg4wruO8eyA4TDHGIFJJN5ZwNHbFqGgrkuubi559uxp3PRvKi4uL/BoBoMx8/kcIQRJoiiyhJvraxKdMNufEYJHJ5HAIIRgsViyXK44P7/gl7/8FUdHxzx97ymnjx5xdHTEdG+MSROyNEUbE1EOFR0F/WbWehsLNrODu2DCbazbTRi7CSaEDeMvxCLbSHOPUSRv9cnCZrZ7P+h/H5X1N4lz2+O3YB9t9b03mWcbzDbZ7jsQEvf1hXYz6rZD2E0Mt8E23F18DPh3crJesJF0/h4Y514nAARutwZ326pdPO773qTba9kMle9XAPfxvODvX/NuErw3aN78QuQ2oT7A+u53LPE8UgiCc1ECuqxRmQIRNz09PSZRcUkqkeDjOn5na65vLjjYG5KkHUIq2m5FmgWulwt6tyYvPNVyTRCKfCAYT3KkSNHK0DQtTdsRWsd63TCfrwmyx2TQVi16kyyE8mgt0YnC6AQEG1mCjSmJ0VE4zxjyQcbB8IjpaMR8vqKzHV1f4UPDdH+EsxMSOcQ6g0kmSK2YTFJc6PnmxRfRb9gJBnnBk/eecfL4KXvjE3wwJCZFKAPasazg5uVzbF/TuZYiV3EQ6SVoEZe8pKRbVHQWmrolTSX7swlSBMr6hiBaqnodu04t0CbuxQwGY5pmxds3L3jyaIigRYkM23tGozFKDfBekSU5bbXEthXd6pJ6+ZbZwPNoZpgHzWXnaKoaQUB5wAWGezBMBYXRyNARvI/S5NZF8TsV2TSdt2gCMgmIQlG5mr4vQUSTnnma0laB0ahgOkzIi5RXby+YzHK+flvy/geHjMca29f4HqpVT1cHkkTgelBZwOQxYfVdi24lo+kI0SsyPUEkhuHRHsP8EY9OPmY82qftaxarOfv7MwbFkN45vnnxJcYYssyjjUaoQFCgtaO6OuPl6+f0veXps2ekwiCTFqMdvm5QOjDZG9N3DfPaRVG81lBklixJOXt3wccffcpqveSLL77i/Wcf8tOf/iFV1fD23WuyPCHNDDc31xRFTmIyuq5HqRbnA2mqQUiavmNdlghfc7B/wE9+8rv0neXtm7d0bXsrlFdXNV3Xs5gvefHtC0bjMcfHx7z33hNOHp+wP5sxmU5IkiSypHZ8Wm6RDnYZiFv7YsntIPQ2BmwSgxCwNdaxFrUh4WxVVLfLb1vYCR82LMSHBet3UYnf9PiNk8LDHYXtsQvb7OJZUtxPCg8TwkNRulvO7maIvPuYwAZPUxum0EZ+miAIfmu6fZcZfdgaaNxBW7uvYTfr3kJFD64FImFgV8Dq1qd6Fz6TkiAfJKida797/75/gL0933aoHs9nIxbpJVIkaJWjJATp8aJDCYl1PVKBEgplNAqD8CnOOUyiCd7iXIcUfRyujhPaXtF2AXqwVcOwUDjX0DmPs1E+JMsHmDRH32Qs5hVBdnjRI2RAm82sSHqEtHSdpy07sjzSMUNw9NZFSMlpqnXNBycf8sHJB0ivGA0D8/UC2tj6133Hau7wfaBrFzw6VRyfPCHNNK/ffUtZrbC+R2jBcDJib/+A6fSYNJ3gvYQgyTLJMIxZlFEio+0dQUm8gM5ZsmKI0prBcMDl1Tu8iKSGq8trhkPJ0bEB0RE6S2cbhAgxkFiB7BRZBpKGRCcsmitc9wUfvp/j2gHj/BStUrTJkDKh73qMUgjf88uf/Tl2dc7RKGWStzDwyN4w9y2ij/Xh4VQzziV5GtDK4/q4ONX5gFaKru7IMkF5vUYaSAaCPFOIVGzkMVrOL17z+OAjDk+PsXaFyTydKxmqwOlxRj5U7B1PYAAu3GCdY1SMGOUT3r17TWIkxVBzcDJBhcjUwoLWCbaSJNmQVO/z/gefcDD9EK2mtK1HCoUIPZOx4uLiK3529o7PfvgJJycpL1+9ondxn2S1XPHRD55xcfmad1dfc7N+R5bmjPY06+oCGyo8NTqB3rYoKZmMx6yvS4KNsteDdEK1XjOdHPLlF7/Aezg5fcIHHz5DKpjPrxDCMdvfw4eexfKK/ekRg+GALB0AkmIwQIjoiZ0kitnePn1r+aM/+rv8g7/3n9C3HV9/9RV//K/+NX/+5//2zgTLxv2Krmm5rM+ZX13z7TffMJwMOTk54enTp5ycnHB4eEie5yRJQpqmRA0xeRs37tiRERp0t8tp8ae3RJVNsI9dTXJbEG/3FrbxYit4J4DgdYTkNrc/pKr+ugL41x2/cVL4dWvUuwsYt4Fu54XsBsFtUni4dyDUpi1CbLC6OEu4t4SxscvbSkjvHrudhZSRD+ycu2Uo7d7v+4Ywu9DPvQHPr7nvzpnv7TneVgS/wfv5cMaxm+WjXIciT4c8e/IhPjQsytd436FUdJgKse8nUSlGGzKdYTvFdLJPnuTgBV3fYbuWIDskHhEcikCWGnQyQkhBU5ebBaSCYlAgpaezFV3vsV7Qd44kS6JIXN3HRKPiELFvPS4RUW8pidcdPNjW01U1o2REZgoWyxVFMkBISW8t66qmsQ02BNJkyLOPfoc0GaNUjtYJ3jsCkcMegqUYZMxm+4xGE+bzFSdH+7EY2FRjSivatiUQyPIclQiE7iPtT0qKYkDfOzrrsA6GwwnZ0wnj0RBlPG23JIgOVISyZNDRVQ9B3wcWXYnwlkw7msozmRxwNP0Y2zdIkUf/W+tQCGzf8eUXn3P25lsSO0dXoGzNQFtEDtkswbcOGQKPjgfkqUQrTxAB50Rk7HQCoyAzCV44irFESIuXASksUjmMVtShZXlTMcqPmBRTMIrGNkhjubk5J7gWqRISlWFFiZRremcYDvb50ac/5s2L1zgPTilsKSmyAcIpEiVxrcVLjXWGNNvjePo+RXZMbxNaV2FdR+/mXF5f8eVXf8Niccly/TWjyYCqXbGqcnzwLJcLUFdc3pyxWl8jVIsyCkSDpybLJS4Y2j4O/Mt1Q+g0w2JEP8nBZgTnaaqGLmk4PT6maVo++uB9puMRq+Wcm/kVh4eH1NWKql7ifUc+MDjf0nSCNBmgtcSHQDHIEMKzvFkiUZgkIbhAnmcbSNFspEy62PFvPEUQG2aPt9RVybpacXl5yVdffslsNuPxkyc8Oj3l+OSEg/19iqKIMJrczB+MuUUQolT9QzOuTRLabjTDLSKyGzfxAS9EdDf0AflgT+KheuturPkPDh99Hwa/vdhthrpLCrGC3v5s90U9nCdsk0KAjfjTnabI9tjSrLYDZPEgKeye+94bzK787Pe3VA/VTW/vt0nc2+8jnHR/KW2b0XnweB4khl34afvE4RZygt1BFJslPiUUQcDh/mnUnHm5xIuAziwisZtloYDwEt9ApzzBSlKZM0gGCGGYX9+wXCxI84D3gmrRb5bJEhQdr1+9w/cZ4/ERaZaipaRpaxbLK6y1HB0f0/cVTb9Gqtjj1nVJ1zX0TSAxGm1ylI3uZpF5I8HCyIw4OThBCoNzkSiwWC65mS/o+5gMurIkTTI+fP8j2jawWCwYDAo8DfP5GdAhpCNLClbLkj//s7/iD3/3P6PrO7wDk2jqck7n1ySZJEkVbdcxHecgFdZ7MlNgVMrFxRW26VFywONHz8jSAcNhytuzL1hUK3TmCUHiHQgvgQTbRgVUowyu72mkY1TAq9e/oFn2HEw/ZTJJkX0CIY2WjVLw+sW3nL95yaNRZJIp3+JCjwkto1GKHue4viXVm5mQllStZ1lbhsM99mb7LG4uoZBkhWQ8lNh+iRc9+cBgdUD4aBavjeRqccXedJ/90yOamzcI12IE2L4hNdHWUogeIz3WdXRdw/vP3mc0GCGDwAjN2csb1ouWveMRj569T2lXmNywXKzpsoAWA7QYUAzGzKYHXFw958tvXvDq7ed0/Q154TBmSdXMSQrFxc0XGyluxbuLK6zrEapDyJ6s0Ejl8KEjSQXX85KqXEc9pXVHtVhQzhMSpiiZ8O7VGUollIuG68sbTh6d0tYly/kV1nmmkwF5rri6XpBlKbP9MV23QpIwyhOCaAhEZWSlBEYrjk/2KVcVfdtTrxv+u//hv+df/PN/ET3LtSYEj1JmQ7G++6zHvQUPUuD6nmXbsVwsePP6DYNBwWx/nyePH3N8csLx8TH7sxlJlmGSBKUVIsQ4ZxKzs/MA24U2vxOjHsak2xmq24qI3rGWHpJmtnDT/WLzu3Hz+45/L/hoF3LZxex37/uwG9h+fydhcYe7ic3sgB1q1y0tddtOPbieX8cm8t6jN+d0G6/Th/d5+Ljt17szgO+cZydB3D0XG9LZAz+J73n+u2OrrBJ2cskukyl+rZXGOoeSmoPZETfzGXXngIqujYYk3ge63lFXLVmak+ghy2RJKhdIYbi5nEdmRi+xAdY3gaaRGJ3SeahWNwQ3QFAQMCjbU7drnOiZ7A3Jh7BcXXF2tYj+zlKRh4RE6zgrbVqqpaWvBIk2CBFnCQbFj3/v9zjYP6CpK5RSXN/MeXtxwfV8ST4YcHj4iL09R9/Bt9/+it5KimLM2XlP06+YL9+wXl+DDHRtT7e0/PiHn3JwsM9qNSdNUpIkwfkSqXq6foWUnsSoLYuc1GSooKnmUZytax1ZEs2X2rZDao8ykA81KjH0PmB7T4MnOEHvPFXVUORR7TTQUXU3hNAjfYKRI0ajCT6kKKk3H1JBojR5kpJIj3Q9iQC0QOeG1CT0TYuQm2EiAh8068Yi0gkffPZ3+Ojjj/jii7/m+vw5Tjt0loCo8VsIMfQgJM5blE7xocPRc3B4QJtYyrMK33gGJhrYeK8JXiB6R/Ce3rZkyZDf+93f58/++M8wMiVTI67P5jSNJUmH/Oj3P8NJS7V4QbCO0HuccngVGAwyHp0e8uotWLvAuRsCltF4Qj4qWJUl2rSAR2lDIjwyRD8N6wTFsMA6R12XCGMJwWOMwZgE2zVUpeXqooPOUSQ9q3lJU7fk+Zy8yJnf3KCEZG9vD4HYkAE69vZGZFlKCJ7Li3c8fvwek2lCWfbYPkq/QCAIh9IpUnmk9vzz//b/yZ/8q39DWa0wJkpNGKOiaZXWd0ulbG1xw4aKKlAq4v1d09A1DYubOW9fvWY0GnF8fMzp40ccnZywtz9jNpuRZxnSCnrbR9VnrTfzT3cbzG/p7NvCdRsT5X2vl92EsI2xu8lhtyD+bY7fatD83Z8FvN92A3edg5IKrfTdtPwe3er+TAEpENEk+Lbi3mUu3WbpDRaHiG3g3WuNtwa/CbRIQoitvxQaz1ZK4/4gfPtGPkwWYucatt/HzuH7TS4eElJv7yN2daHudx67j7+dmzxo97xziCCwLpCYjDwdcH39krq9pvMrXOiQQuKDou8cuYlG68+/fcXbV0tGoxnTyZRhPqAql/jOUZgD9gaPyAcTnFeMhhNms0OyLKd1PU1Z0vua3leU1YLV+pJ1tSBJQYgoA6yNIDEpWqbsjROMSGibnvWyxPWOVCccHh+TiBy84tGjp9wsbuj9FfPlgt55RNPRbHYRyvUlX3/9pxwdvUeSKG7ma5AdPjQgehaLFd4nBJeSJkNevXrBaBRlI9quI0kCq2bJ2cUrgqpJC421UXs/M9GPoF7X4DyJSsBFj2OpNKvVgj60pLnCCYdtWoQ2SAOdjZ4f2pi4QGR7EqPxrgN6WCcoRpycvEehp4TQE0L8Gy2KglExJNMtuVE8O5nx7vyMVsehQdM30QjGKEQqqTtLY+Ho6DHTw6eIbMIHn/0InQZseUlty01V6ejqmpDnpMMRrYuyLsZomq4i1RlaahKdomRCt65RuSFNCloncY0nyGjtWnUlP/7RT+gqy5uXb+malkE6QQnFu5dX/M7vFmSpYDqccnn+li+/+AWffjImz0bkmUKqnIuL1ySJRyro2oqmuSEfgzEO7xucb8nyyB7yProIzmYzppM9VqsV19dXFGOHljAeDrFtoDswDIzBOMvzL+fUixsO9g7Y30sYjYeMxxNOTk85vzjnz//sz/id3/kdkszgvSUxkqpcUtYrXOgRskMZS17AYl7Rdg1N00Qdr9bhezg/u+EXv/hretuSJJoQoG1bBAKjDEJKzEZA0vsNoSJAkFGDTIi4CxTY0I0RtE2P7efMb5Z89dU35KOCvf0ZT5484dHJKQcHB4wnI5RSDIpBVGcg7oXc6wjuFaDhNg5ti/HtztfD2BFC9GfYrgn8pt7M2+O381PYRMDt5DsGL7+Bfe6GpdEreTMw3pbDgo2j0fY5NoFxEy93O4KwSTJ+42rhRdQUj5u+G5vOyPHkVkaCrSZP7Dq82Ez5w13g3Q3G35dF7725bFo0sR1Ib9RCN9r+cgsR3Q6fHz7HVjBvOxS/RziIg3i5s70Y7lJLCAHrI/tEBA3OkJopvitwTREHWdojdYRQrA3YXhK8xnU9QcXXfXx8TJIa2qbdUC8dShm0yXBeEHDY4JhXNyDixqkXHWV1w2JxQdfV0R3LQ8Djg0XKuG2upadINcJLUl0wTAu0NmQ6Zzbaw/YxUffWcb24IsieycGQb778lrZLkfI9Dg+PGAyG8Zpkgu2WSANBtnTtAq08SniC7zm/uObdxQsO9g/JC8UkHyCAJM25XDY0zZp8EgAbq3Ci5HBfb6pEVRBkQrl2nJ2/YTQe4EVN667QWYOQHmNyUpMiaRF0NLUlHxq8DRhlwPW4vgMh6Lolq8UZbVuiVJy92OAwIrC3v8fyVUKmPa6vuZnPqapq40C3ZZEI+q5HBE3b9IxHY5q65Be/+Bnv+x/w5Mkxjz74IYvrN1yefUPjOjQpfdthrWNvnEYIIwikEaxdB7WkvqxYvC1J+gZfN0wnhnwqEFJB79EaWuvp+55ipPkn//Sf8n//v/3X/OqXX6K0wvUteUj55edf8f5H77E/PSA1hnp9w4uv/obPfiixjcWJJcFXaB1ou4ZiYLi5uaBzddwy1gm9s9HXQ6cIkTIsJhwdPmU4mvL8+df0drVR4nV4LzFGMzsYobVlPBxz+miPRIz4+IMf8/j0GdHeMiVNc56/fMl/9y/+BZ/+8Id8/c3XDMc5+UAzn18CPaPJiOevf0Hdz5nszXh98YKb+ZKry2vWi4quCgySCa+ev6NuKtI0QxOVS4MXsVPvHWrjaSDFxvBnI1fhomMWajtMJooDBuLPRYiJwjpL1dRcXF3x4vkLRqMR+7N99vdnnJyecHhwwGQyochz0mwDQWqF3nhIKyUJQuKDx9sonXKLsPgQ5eu9QwaJCgp3a8MZY+SdHcFvNuuE32amEO4Swy2mH2ILtV0223YJSAlKgJbInah5O42/pXtunnNDqdwA7fe0PG5BN+IiT6R5bZQMxQbm33o/307wAQQev6GAfbfK/z5oZ/vz24QhQMhttt7uRNx1TUJsDEP91l4vbJLWHZ/gDncC/I4BEET3tU1ijB3RZnhPZM/YEHAOulagwpBUHlC2FiECMnF431J2DWVV0azB6JxPPv2Mjz7+AdZZBsMhIEjSgt5FHRYpdcT/nadslnjRIlQHsqVtVoCj7VYYLdAiwwdPj0Inm2v24HxH19QYERBe0TceScLB4JBhHuUXhuMhzvVcXp1zcfmGdT3HJIonTw8RPom+BZ3DWZhO9iF4bF+D6/G+gdBRpAl+VGC9pe1WLNdnOLdGykCa5hHaGSkuLq8wqSIvBCLxICRaRGmUqmxoa49WOYdHT/jqq+fcLC6o+ivygSQpPGmWgYgJ1XYWoyGf5VR1icQTlMF3AS0Nwnica1G+pXMlq8U100mNkjlCSJzveP+DZ1x9++e0yxuka7i4nlOVXUz8IaBEiGSIoPDdxtjdOpAN6/UZ795oTCIZjAumpx8hh3t88eXPKfuKVTWnpyN1A2wCnbc4IUlkQqeG1MFC8ZhhIXnx9V+xXHYM3JokTzEmxYz28AGyLGe+XGC95/H777FsasbjMcNhwau3r8jTAVqmCCxGG3Ae19zwzS/+DBU+JB15fLuOS4JS0LcNxSDDtj3DYoz1DtvX9LakGEzQSjOaHJDqCYnM6Jo1zpaUVUmSFIRgUEqgEkc+6qAI5OOEwiiK/Q6fluR6n7aztL4hK3KuFwt+9dWX9LZhrx1wmuxRNdcIOkZ7mvnNNckgUOwJls0brpYXLKuKgGY8mfHiy5esVx0H+0esrhsa16NVgpFprLalx/V9JE8ERwguGidJQbCb+BA8BEg2jLcQAs4H8FtMXyBUhFu7tueyueLq8po0TcnzlL29PY6Ojjg+Pubw6JD92YxBUZBl2Wa2EcPpdtYY41ocMt/GrC3hwgiE90glkU4ibE/o43Vu4aTf5PitNpq/D5d/uG8gpQQdh8dSylvGkFIKtQmIt2wiKZE73QeA2+DBgvuqorfnvGU1+XuB/S6BRKjpvrjd3X2+rzv4dQyj751DbM8gtnLgfgNn3TnL3ScobQbLhMia2vwSA1GiIiZWeQ8y8z4GcNs7utbS1h3Lm5LrqxU3NyW9W4GxeBqqtsQTkOQcHU746ONPGE8mLFcr1mUVYTSlMUlGkqZok2BtR92U2LrC+QZCj3Uldb3CuciBHg0m5NkAHzzXS01r15i0wNk+buOahL7zGKlRSnOwd8BoMCaRhtE4JUkFvQ9cnJ/jfMdglCMkjMZjqpXF5Bm9s4QAXdczGY9ohKdp26gKKRSzgxlFU/P8xVtm+0OCcPR9VBdtupLLm0vUumO+PKcYGpK0w+JxwZMlGcJL6rqmbhzHRwc8evQYbTK+efENWSE5PN4j6JpATZ5rumZFZxuSNEFIwWiasp53+AAmzTFS0zYW27ZR0E62ccEtkdjOIlSClIr1aoXzAaMUro3CkGlqCD6gtuKLLgo7dr1FGkVwDukdoa9Zz9/x9nXPUXiMSguK0R5PP/hdFssl3/7bP2VZlRw9HZANckRXIfUeSXpMnowYPjogTFbUiwseP/09zs5e8/JszmgC40lOlkryvTFJVmAp+cUXX3J0eML/4b/6L9Fac3Nzxe93DdfzK6yzTLMBXV+ipeHq4oy/+PZbvv36r5kdFZRXF2RDyLUmqAzhPFp4jAp4bxGhJ4SertGIZID0EhkMRuQEqzAqB2HBK7yL/iRSeJTyBNGCDNhww6J8hTYSM0i4WZTM52uqsuMHH7/P+fk7vLfcXJ9xcPh7GJOynK/oW89gMGE82qOua3rbMRgWlOuGpnZ88e03XLwu2R8/YTTQSFvi7A3OerI0emEIxIZEE6iqiqYto6y8C2AtkSXJvQp+C9WE7afeR9OrbVEcQsCFWJR1bc1queTF8+dorZnNZhwfH0WY6dGjW5rrluq6i3Js45MQEtxd7NjGYaXuIP1dE5/f5Pj/etD8nUC7oW4JKW8n9g8Huvd+Jja1dAgxfm5hpJ3b2bn/LhV2e3xfYN+9nt3r/r6v77/J98+zezx8nWFzzbuPu2MU3b3eDado5/VufrLdrYh33KSxmECc83gbcL2nXFVc38xZr8oIQ/gW3zR42aOMIjWGvJjy6Q8/A6m4uLwAETciEYIiyygGeWyrBATXYcMSZIXrK9q2om1LnHdkWcogHzEdzpiO9rm4uOTs4hzICcHhO09mJvjeooTCO88wH3F0eEKa5KTakA8ShITrqzlnF29IChXb2ADOW4SOw8YACCcYjoYkSYqUUNYrhFLk+ZA0yzg8PSQgWFct+I7R3jFpltD1JVkhuJ5f0oc1kxwcHcUgp25q+r7n+npJue4Yj2dRFM12nD4+phil1O2a4SQFneN8zWBguLlpMYA2gqqqyIvooeA7RV/HpUmTaJxVeGfpbUXnSpSOCq5yM2Qfj6YYnbFYlIwlKCE33uIaqXSsdl2L94LaxooU35OqaDHk2iXNuuf6EgZ7J/QhwYsBTkjy4fucL77FiwOKfA+ZtiT5HoKUsuzwZYmxHQSFNmMOT3JM0bB/8j5pMaWykBRDnA8MJ1N0dsbz16/5yU9/ymg45GZxzcHRIXsHU7q+ZjjWdH1FX7fcXF7SrG44e7FmceFICkuWJTjvsMJTtRWjUY7G48Sm0g4eb1tk4qmrmr2RxFvBMJ/hWNDjNxpU0ZNcKUjSDR0dT7AtZf0W17csb9b0raFtLePJHp/84B9S1z1ffvkF7969oWssrpMEZ7i6LHnv2TOm40PenL1CyYTRcMAirbl4fcH19YL33vsByo+5ejen6zv0RqSQEDfZh4MRw+EQbQzrcs311SWr1Yqqqgiho+u6SAnd6GVprePQud/QobcU+nAX40SIfw+CQHB3s9HWNpy9fcvlxTlff/UVh4eHfPThR7z/wfucnp4yHA7viDoyFuFbWCjaduoNZLQV3VRovdNNCPEfPinsBsvdZbBtZrqlngq58UreCrLtTMw3QfFeBS7uB8ZtPN3NuveOsOHw7iyfffcuO13Czjl2X8uv4+7uvnHfR8O9u+PtVd/qoISdAfjuKbfS3G4DE4UN7rdd8Nv+wWzP45wnePB9oFyVnJ2d8e7tG8pqBTIwGo8IIsPTkeYaFxzjvQnZMGVdLyPGbbvbpJAJjRcJ1kZz8aq+4mbxhrJeUrclbdcgpCBNMk6OTxkNZlSLlpvrGmcTpC9o2zVZmiFVQpYkJLnk6vISLRST8YzRcBItE4sMJR3OdVxcnnN++Y6T945IlYrbzK5HqQKve7CCsqkZZkPqtkEJUMrEvQjZ8vLlK4bLgrJZo7QmSSRFniMl9LZiub7g7OobBhNLay1SNPR9lK4+O7tiddMwzMas1wtuFnParuXw5JS92ZSJzGj6NUFK9vYO6F1F17f0rkL2kWatjQIETgq6tsI6QAVMqknTjCQkCNnjfUciAxqJDnBzccX8eoFWKXVTIU3klbsgUUFDmuO8YbFa44NBOwHC49qONCjSNNA1jrO3L3mUTxEZBJnhpeLk2Q85evoDhtMhTW/pg0HbFJRitbri8vmX5CJwNJ1gpGG5bHFk6GJCrxMGkxEOQZZG+873P/qEq6trghAkWcpgPGI0HmKMZF0uECpCKl5Y2qoiU5Kj4YDRMFC3VxTOsygb6r6lmOSMiyGauEeTKoUNjs56CJ6ubWPn7CWnx4/hasHVco63/can2iFlhF687/ChR5IglcO6G+aVRTLi+PgJg3zIuryAkLA3HZGapwQnUKRMRsc8evyELBvQ1aBIWC8rikHO8fERh9PH/MM/2qdbp6xvYDFbc/7uBb/85c9pu5bEJDRthdSa2dEBBwcHHIsTDo6PWC4W3FzfcP72HddX1xDuZHu01rHQ2XQXW1Qk0vPvaKJSii3CvTFmEhEy956m6miqmtViyeX5Ba9evuSHP/whH330EXt7e6g8kmiQ8paqjwAlopeHv4WtNjds5qLRGvQ3myr8Vstru2yih1TT3X2ELRyye/ttsN0Ew93A7cX9bPaQHhq2NDDY6IV/F9r5dZ3Cw4C7e527j3vYmu12Irvdzf2kEVecYoLwG82lrfF3IBCrn+3wXEoZh+g713a7Ocld12K9w3eW5XzJq5ff8ur1c+bLS1TiOJjN2DscIpTHhWiS09mW85tLfvaLP2Vvtsf+/l4cTBGTwM3KkGY51sb3tu/X1NUVnWs3dVogSQrGkyEgWS0rinQPRUaiPe89FgTfc3pySNNULJfXNE1JuWw5PT7hg2cfkCY5SZKSpQbvG6q6IUlzisGQly9fcPJohskN1nt8gMvFW2aTI3QSSBKNtY62bRlPpmQDxes3X+O8Y71eIbWgKFLSJGU0KqKmkq9YlmckWctgbJDGE6Sn7WqaxlKuowCaNAGtBNIJ5utL6pcr5ssx09kInQqqZsmqDFhb07QWhNrMhDTQI4TDC0c2kPRNT1s3JFnCKM0p9JjD433yXBFaj4rcQh49eo/PsyHXV28ZSE3Zxi3z0WgPLzSrumPeKS7rwCDPoxWnjvMrrRQ6zdEKhEo5v7hCN5rSzel9wmR6wMH+o6jVIyyajq5dI2WL69YMBrBXJLh+zfLGIs2A05MnjA/3uFytGY1zkmSE1ilt23P63lMeP33G3t4UnWhOT09o6jXL+TU31xegAvho1PTRB+/zaPIjXHnN4jpSVf2ywhCYFROePvuAho626hBabWYSEQO3vSUZpdFz21uKUcFemDJfKxpX4UPcvQnYaB2qU/rO4XqHED298/EzJKBqzrm5PqepAlIU/OLzrzg8OOX46JDBcMxwNOLw8BShDMp48iKjbtaYTDCdTBEuwXc52k34+pdn1OuSH/74U4qB4dvnLzk/v6DpG+zac3GZMBgNI510MGD/4JC92Q1927GYL/AbCmkIIZpbdd3GK2HzeSaAj6SXGLfi79gHH+cCYaOVtAnyiTYxxgnBcrmkLEsWiwWXl5d8+umnvP/++xRbhzljbiGpOJOMyg5as0lACq03C3NeIaX9jWL9bzFTCJuBLreCdVstn8gCEtEdSMVf3BbyETHy3Qba28p4MzDehtiHFM2HX99BPPL2DefBY7df/7q5wfdBWd93nocSHLvb3Pfuv033t891h+PFBmVX/C7cymQ8THYPr9dZR1NXXF9dcHH5hvniDJ1Yjh/vMxgbVBJQGpKsIC9yhPRYU7Iql5TtBe56jidWDCYxbDnNQugNHxwGI8MkSbHe4ZwnS6ORed/3HM1OGOWH4AxKZTwzz4AeLQWXV+8YDccsFze0Vc/J6XsUwwneegKKpnX4AM4rprMjPh2kvHjzBcZ40kSCtdTNAhs6tIaBnpHlhq6XBAFKg3UdXmxmMMQlOYJFSSC42CVUl1i/JMkD0vT0vkLKOORbr0r63jMcZCSp2myzQpoKpLK03ZLzi8h0cq5DakgzTWIGFIOMPE8pyyVt1+Bci9EdfdeSptE7IlECqUEbTVmuCYegpUD5KHedGMPV9Ty+F8KRasFsb4Ya7oPOMElPlvVkaoANgelsQqICfb2kbCpYWYLt8Mbhc8XYjGmw1N2aJvQU0yFaJhid03aghSHYmuAsXV1Ro3FtR99rJuMjZgcHrJuW+XLBaHaIEYqb5RohYDbbJ9WGNI+wT9W3XF2ccfbmZVQhHWYUSYYQHe999hnN9VvevLpieXFFYjyusyR5wsnBeyQMqOpoaSqNQCiPlAoZPNZufFRkoOlKFusbymYOOJT0eGfxrsP5HpyMdHIno7aWV6hE0rY9yliCqEBE2018S55JskyRpIYkTVgsV3zxxXOePv0gsplyz/5sxvn1a765uiDRA9q1pJoH/pv/+l8yGx3wT/7xP+EHn3zEwdEBf/lXf81XX35DkjgWywVv374hTVOyLN/ANGC9u4s3G+KMc+52vridIXjiZ8+7OFuSUrO3F+ccTVPfUvq38c0Hj9wkmW0cury8pKoqqqoiTVMeP35Mnueb+29IO/JuZhFVXG0swDe01Pj//R2sX3f8Viqpm7i3eXF3GPk2UUi5Zf+IWwnsbRK47SBCIOw4ld0+/YNgvhvIt7cLuB3WPkwKux3DvQDONnh/N3ns/rs7J9m9ht3n/c5MYpMYtgnhO4/b3CVsl17EnYosD84Dd4nPeUdZrri6ektZ3YCK8sZBV/RAb3ukCHiXxu1VqZnMNDKNHH1lHMlmO1wou2FTdSQmJc8U4+GQyXhE1TYslguc7+lth5KS9XrN0UwwGAzwvSFJCvqmpmk6vFHYPuCDZTAY8/jxU8bjPQIaoQTGZHgCXeOxQdN1gcW8RApNYgJCOrquIgQHUrBaX5CPhrRdTZoNGI4GvD17zcX1GU27QpvAer1iNnvC3myf83c3vH7zAmUg6JrexftUbQOiQ6Fo6o66bshSTZqmeBxV0wCgTSBIifAbQyeRRL194i7IeLxHlmXkRUIIEh9uMAbMKKWpBPOrOXmhSLVGewMkEDR90xFcz1dffM6v/vpnNPNzruYLTFBk6RhTZLy9afnq/CXZeMrh6RNOnpxyFGl2TCYDhOvANpy9fsnq4oJqvaRtHMMiofOejhZH4M/+4me8O3/D3//7/zGT6T6j4ZRm2XN9/oZhPuTdyrG8XqGQGD2gvVri9BvMaI/JaIYQhqpuoyPZYMhgMKBal/hlT5Fr6rpktbiJsh5pinQWEWA2GZOoQOksiTEM0oJqeUWSpwgK9oenBJ1SdYK2qcAJPAFvtgteCV3Z4foe6ytevP6C3l4iVYsIDnyPEgGTpFgXiwoRJFLkKGNiEZEAosUHhTEJwgek0nzwwSmpGSJFT9/XNF1D05a8fPEN62rI3mHO+dVLXr39msFggG2XLK4avvj5S778+ksK84rxZMDf+YO/SzHI+fSzT7DO8jc/+5z5fE7TNFRVzeHBEX1vOT+/4M3rNzRtg5Zqs9x2F9i3Xf82lthbIzJJnhc8fvyYqqp4+fLF7WPato0zA6lvOwUA7+KcYL1e86tf/Yo8z1FK8fjR4zh8VnKDNkQGkncuRj2pot86W1ti8R0Y/dcdv9VM4eH/2+NWl4PvBtJtMI98/PvPuc2GQQAy0qzCg9t3nyvW3bcR/jsQ0sPAvq3Yt1/vHg8F8R6+zt3nfDhIvr2+DW54P5ncsY9uKWlCIm9d4DbpY9MliM1r8d4jtucKAe/i3oDUHp0BpkemcWAXVI8Xlqpf05YLjDF42SN1hwgtShvSzWZnFCYSWOtQyiKkpe0aFovAslxT1TXOOzrlILRoZVmXK7pxi1GKtq05e/cGfE/dlFxcnqEU5HnKo0eP8EFgnUcKjUNuJH8lOskJQlFWDVVdk+YpRiqUjH+wznVIqaiqJXuDI5JUIqRnPC148XrBYJzhQ0fqM9brhsX8FYvFiunMMtkbMl+e4VVJ5wJCWrJc0/WOqukAwWQyoe8tTddgbRc9E/LYKW0d9LTZWMk6j5Jp5JZ7yXrVUlcdzkbjeCE6XN9CsBR5Tp6MyMUhKSf0reTs3RUXL5/zx//yT3n1zbeIvmJSKHrrkVaQmhHz5Q1l6zg5GJJNDsgnM0yWMRwPUVJQrRcc7c8Y7p9S3SxQ1vPli694ffkObQPZqKCvarSSvHvzkvn8EmU0aZJTr2uWi57r84rj0x9TrRY8Oj2h7y1IQ+cERTLi0aNnyDRHJCkmSei7jsViSd+1ZFoSescgTRikhsRn9E3J21fvSLKUp8c/JTUKc3JKGiy1MXxxvaBrYtB//71PeXF2hqhL/EqiUo1SOb4jOp6R4SqwtUWbAH23mSEl0SMbCw4QCq0MtofUDJhODsjSnOXihnW92lj9dhuarKNaz7Fd7AaXqwvOL7/EesF4NGG6p1G64/PPf4UNNYkxJCYhOI/RCV3XkSSG3/mdn7A3m3F2cc7x0RGDruCHP/wUgeDli9e8e/uKd6/fkKYDlNS0TUvTVBGKdZbgw0bqfBMzNp9/fztzlLcQttaai4sLvHcMBgO6rgMgzcyt6F3fudtOoG1brLV451kvV/zi55+zvzdjMhqTpSlJniNEQMq75eDtfhfcj2W/6fHvZbLzfYF/F6OXW9ho59jCLdvrc1sHIcKtfefdsDpO7G9dy4S423dg8xzfAyE91DG6reYfdCS3tz342cOvH77mh/cRG+qUuG2QNkH/Vk6VOwYWcfvU+4APdxZ6YqPwKoi+ygKBEtFjwiQaqSVGSpJBwngvA9PQOotzDc4FOgt1D1LGIZNzFilBdJFOJzfMBO8DbdtHSmW/QAS9mSbEP90OD1jGwwFCBHrbEKxktWqwfcNicUXAYYzC+o4kS0DGNloh6YOj6dYIJQBLb1sQkYLadjdcX96gVy7SQAks1xWjfExfWtbrBUJtRMFdw/7RlPOrN9RVSbVuyNI1iijRXeQGqXt6uyYfSFxoSZQCoeIHLIgol2AdZVmhjSZNh6RppM4KKUEKus0S2HQ6YzgYIYTE9T1l39F1LUlqGBR7SN1SrSv6tmNQDBHOYESBliP2J+/RrVMSXfDXf/mv+Ju/+RsGiaZrSpyFUZFjBlMapxnunTDNMw6ODskHQ4IQKKOx3mMRJIMxZ/M1XS84fPw+XbniSILZ36dqa4pihqDm93/yU6RS5EnCm9cvePT4MfPVkrPLBT/4+Af8g//of4G3PW235mZ+jjbRUCiIBK1SEIaqqimriroqWS4XjAYFvfAMEh3FDoXHGMWbd9fYas3xbIzwPZeX1/i24eDwEdZkvH3xjvlygQ+e/+G//9fILEGJgLZRTykYje0s1+slKhXkhwbTGdp6ia86dK5xvcUFgQhRCt7ZuMUvSTk8eMzJ0SPKck3bNHS2iTBT7wjEosLoKApXlpe8ePmcd+c3HJ8+5vGTPdrugrcXl0wPpvQu0HYd1gaa2mH0gJ/+/t/jP/2Hx3z28Y8Q0kSP8aZmNB7z8vkLfvCDj9BC8adnf0Zwgqbs0MrEmKViEeucQ8k7n/ptlIiS/2xmiJFRqDe+MKv1kjwvEFJudq/YyLYY2q7DaMF0OsV7z3q9vtvCdo7lcsmLFy949OgReZ5HG+PE7Mx778Pg3rOhp37/btb3Hb9xUtj1PdgNrLtDZ9jCROouXe4G1Z3AHSEVTzSaexB0d5lFYRtY420+hI0j233YZTcpfWev4AFUtD3f90JUD5LG3z7Q3mSE285ie9K7vYXbQbkPOGvpN5S1GLAVxpiYu7bVhJIoKXEubownqWEwNnSuxoYKS4V17e1rCF6ig4EgkNIAGmeJ+j3e0+MQSKzt6DuLdwJB3DoXKkJ/Rke6W287mqairJY06zl1HSu62f4EhOeLL3+FlILR6EnEdXWC1CnOBqwLBGERKlCVkfOvlEQrDSJFSoeRGdZZCjPi5PA9XqzfUTUlg8kA63vevHtBayuyTNP3ktY6urpikBv2ZlPW1Zx311eMD6J2jdIS6xzSeqwNCBHVNvvWIoS+9YrQ2tD3HqTCW8dgMCbPBuT5kCwZ4LG4vqSqVmRZxtHhPtoEzs+/palrlDTgNCJkVGtP2ZQ8mU0wWU4ic3760z/kzYu3vH35LacnR3z6yYcUaY5SKZ///FcIA+/N9sk2wUBrRZJoLq4uEVJwfHxKMZ5S38w5Wy3pmhXj4yOe/fjHvHl1xmQ0IZUa1/c415LmBpNdIoTj6QePKMsSPRhyXTkODg8JNmd985YUT5alLG9uqFtLXkwpbbeBKNcsFjco9klkoF93XNVLBkawP51QXeW4UjFMNV1TRUabcwRlUOmIw5NnrOpvMFmBlDkXZ1cMhEUKRataOizzcs7zd+/IBnt88r/6I6Z6youLC1SnMLmJnbR3BB9lRSRREl2rnDQpqMuW+c2cEEBrgw0hFkDOIkVgMEhxNi4NTvcyRtMnoDTv3n1BnisevXfKYDjg62+veHc2JzjDs/d+yOnBR0wHJ0if09Vx81goyWAwIE1SPvvsM7795lvasuaD95/x9RffkEiDs30M5psKdxv7bNffEWA2dWi4rRghTRL63rIu1yRJQtd3SCkj0y54yqqk6w1pkrK3PyNJEqqqQspYIO4ScN6+fcurV684PDwkzXMyrb4Ty74bo/7/lBSEEPcE7rZBTYpYjUkh73kQbEP1dg17Czv5bYbdBDm34ffeDnGIGXfLw90dZN/ieCF870B4e9J/1xvysOv5dy207d5fSrUzNL5voB2XWTbtXIjJwDuH7TvapsH7KDCntUaEO62STT5Ba00xKDBlwrpbIGtHSDxeddFZTMTZAwSUjvx2229MhVAEL1AqAWvpusg66HuPc6CkwdlA6B3SCEwi8DLgnKW2FW/evqZvPLPpMXuzMUYrvO+p65I0NZEapyQ6MSQmw/v4+FQphFb0Lgq++eC4vLzk+nrObK9AicC71zdIJckHY5rao1VGUQxp25a2rwibZGq9xzrL/myCpqBeW5zztG2FUJ7etfRtjyKgUkVZV1jnUUHF7sDkDEdT6qalaesNzAWpNMz2p2iVoFWCIHpYC+Fp2hIp4fjoAAFU6yVVVWH7qKDaN55MGi7frCjMASIkOCdxHg4Oj/nH/+Sf0lUrDvZHHO7PUFKTZ2MOjp/xz//b/xdi83kwSYI2mr5t0VLyg88+YzieUNUd14sV1/NLrC1Z9x3ZaMYHH36GCoZEKF6/eE6WjTg5PmBZLbm4OGf/0x+wdzjlcnHDD1NDLwXeaIrZFBFqqqam9y3T6QFZnmJcRlbkVFXJIM/o6hKvAtb1tHVNoTKscxTDIatFglYmbkCvK1brilE6YH8wRqUFOhnwOz/9I558/Am//NXnfP6n/xO+d3QellVJ1dWEzjOdTnhv7xHzdzf8zZ/8BWV/xuzJlNHxhGwwRglN1Zd4ITEqBVKKYkyepiA8V5fv4hwSH8ksQHAenUi0MVCWDIYFw/Eey7Ki71d4UXNx+ZybRY4QgkenR+zvPSE1+yiRUJUdMkSJFSUEQjp623N9c8Pp8TEffPgBqckQKLqq5eXzNxidxMQk5C2N3vuoC6Y2HgewEwo2hW00xomMoTRNadt2E1dAyPh5z7KMLMuw1tJ1MQEPBgO89yyXS5qmwXvPYrHg7OyMsiophgO0S5DqflF8HzLaxrVfGwLvHb9xUtAiahkF7qAaBFHMTmyMJLbJQcby3uMI3NFLpVC3Q2JJXP/GB9RO8N7SObfuR0FssbkY8IUUt9IV2+vYzZLbxHD75vjtjOXuNqXUPd+EW4hKSnxw27dx+37eJYRw96bHbx1ux1dZCknYwochigWqbdUgBd45XN8i8EgJ1ve43mGDJyW9HRQ5FznyxTBFXQqCE5RLGyFXAzrToD3O9UjlCfR4oTdNaiAEiVaGrnZUlcVbUFJgbYSrcALlIj3WB0fnuohbhhohDH0XmN8s+OEnOi5r9VFu+Hpxg04TBuMhQikIkr6NW8nRlrTHqEDbRLjFdR4REj58+iOyJEEIz9n5W4Z5gVSSatHz7L3P6G2g7SrK9RIhDFI62qpCSE3f9dTdHK0MXsk4EM8VPliyVNP6Ci0Dfd3gehAyRYaMYTFkkCjwFrwDFUizgmI4IM1z2iZCXIkREBzOdiR5TlHkdG3A9h1lXdP3AZTCdx5hNfUSrr9uOfhwDA1IFFIbFvU1J+89YZQX+L6LjmlJjrWB/cNTPvnxjygGObP9Gcenp4zGI7Qxt/OfJE9ZLFYQPMJLpM9pVpbz15cc/viUVGeU6xISjchSnDCMx8eMpodMJzM++2yGMTkm1ZsFQBCq4OztDb5vGQ9GZEXBaDyic1GAbTweMh5mVKtrurYEqxnlGmdb1l5Q64xS5hy9/0O8d3TdDe/eXuFbS/HhB8jxiCc//BFPfvAjkCkff/pTbq5v+OLzv6GtStqyQVjHSBvSzvLmlz/n+esXXH3zLVWzpDy/Zu90j5Onp0yO9wl9gTWade84ODkhK/ZIjUQGx5xACB1CW4ILeDQCTd02ONsTN/dBKcveNKV3FusrnO+wrqXIR/S2pGnOMFKiswzbglQJnQtUyxXL5ZJ/+T/+S4bjEf/r//w/R5mc40eneAJlVXFxcYXr/aYj8LfQtHUWpNjIXGzj2F3MCCEuqgkhoi9E092yEbcy3VmWYUxC13as6gVKKPb2puRFpJI3TXObSNq25ez8nKura4rBgLzIUMJEeqv3iOBRm6S1pf977uYa/8GSgpQbGGBjx6lUtCrcJoLvsHm4JWPeVvu728W7x+6QdnuE7QQ+3IlNbwO0ePDY3f2JXbgnVt7yHrS0+3ruKv4d0bud88dTbpPALS60EbJjK0pyB2PhCWEHWttsLkZNKEHwEatWXtH3Dut6rLN0fU/btZFKhgDvaZuScrVgsViwqkqE7uksmNSSjyVJkZAqAcJuBtaSIo+WgG3T0ZQ1VWXp241arHBIqVAy0DUtEhNlmL3Dewuezewh6hipxNC2NSE4bBe4vrrh5nrOyfHhre2g7T1G6Q3jwSNFoOtaLi7OWVzdcLB/zPvPPmaQ5jRNSwiez35wQjHMMYmg63usk4z3JlhX03UNA5WxXF3ibZTQ8MKTJJI8TVEavJAoIRkNhiAbVBLo2mojLxxp9cGC7x19U+O7GomPlViaYIymrhqsC2RJRtu2NHVDnhr2JvtY2yGlJgRL0zoIhr5WKJfSLCwvPz/HzXNm+Snt2qIzyevLd3hvODraQ3gYFkNU8GilUUFwaAz/24//dyxX1yitMSalbTucjx3eumoYSEmWZcz2powGQ7qq5+XLVyxullycnfPJJ58wGhfMDqZRclpJTh69h990i9O9MVpruj6K9okgEV5xcTbncH/GYLQPKqGzDmScMXlrkaEnSTRGZ3irkCG7VRO9rloYTNHDfSRwdKqpG8vFu9dkF9fsnz7m8WhGr1IQKQH45Pf+Lqum4+uf/wxENB/SQdCVJV//6nPenr9F9pY8aNp5R6Uqvrz8ikcfPmX6+Jijw0dctw1740MEhouLM+rVJX3XEmTcGwnCEwUaXdzH8QYpo6uatQ3j6ZDExGKq77tNBKoIHt6+PaOZVLSDjkQf8PryDb/8/Dlfff4NZ2+vQQj+y//q/8hksrfZyZkz3Zvy5Nljfu8Pfpe/+rc/I3TRejbWg9uYcGfjK7aQ8m0s2aAIQd1aaQK4sHV1jEhD1zeEEDBIitFG3sIYAhaxibvORSGgi8sLXr1+xWx/j9F4gDH6TkbDRbc473fUoPnNj99q0KyUunU92zoKbTm1D9k62+MhDPN9TJ/dYwsz3fvxBl66pWn9Ghe43Z9vk4LaCf7b2x/eb/tvCNy2YQ/psTy4pgh3bdVad65jp3u5fV4ijdekCcYYeusIosMFkJtltb6NQVMQ6LuO9eqaq+vrDee+Q5joRta1PQSFIsVkZqOg6FBGoUICFup1S99FrwLvFEG4qKSowBiBFVFOXKiNfpNQILfvV4IWilQahtmAXKecX13z7vVbklRRpHkUA5MCRMBt/uCFjB+I3vbM59fM5zdMxnuMZxNSnZOmxQYflfR9vD7bB4pBwWQ0JJBi+0PafoVRjmo1p25KiiQleHB9zyCf8MHHn7Aqb5Cpo2fNsqzIiwInHK3tSHVObjKUFwQXiwIlommR8IK+7uisQ2nDqukgSCajCZPRECNhPJ1S1S3LdUnAkKk9bG9or1u++Zsz2ivFKB3jbZQq14nk9OQJyuRIomvZoMiYX17w+ttv2D884ODwEOctaTFgKwSfbaiFzjmqdcVqvUCqqPB7enzC4cEJg8GQ1SrOONq2ZTabsbc3pa4HrNZLmqbk229fgBB8/IOPEc6TJIZikLFeLUm0ZDIe3C47BaHQJkWoBNv1WB+NkWLBl9D7uHmcpBlaJ5w8Vrx88ZqLxZqPP/6IfDQjG04Z7h1ycfaW2fGITiQondLbSKLIx3v8wR/9A+ZX11z5yAbzBMqupXzzhs52KKlwLlIrbANeeLpVz/ztDcenT3nv4BSRGmzXUa1LqrIkKkVFZh4EQug3pk7bzWFompLVqqcqV4wmBQdHM7IkjZAjgTzXNLnkxasvyPMlWuxxcV7xb/7859i1ROucv/NHf5ePP/qQpo7Lj9PJhKpckacJT5484tuvv2VxswQl6brYJceB7kPq+vfEuajjc4dM7KAUsUCELDGMRiMmkwlax/Ac50+RndS2LUprqqri1atXvPf0CXuzKVmW3fep2UlMuwtuv8nx22kfibsXuGXUPHzhW1nXXUjn+zD63ePh7fLhLsL3JI7v+39XDlsIgTFmI6ly/83Y7RDgLlk89FHevbaHNNX47931xGGyuG127g28Q0AIhVQpAo+XliSA1AoBWOdYujnr1YpqvWIxv6FcL1jMb8B6DBpvPc57lBV4LQmpRkqD1AEhoa97ms5GSKsBGcUGYvWsoixx3ayw3iO0wsGG0bWhxjqBloZEJPjGsS6XvPzqOTdv53zz7WusdfzBH/4+Mpgo00sUMWu7Gm0kSgR672htxbqcI6QnzQzj8Yi+Bds5tEo2VbqLMJCWJCalrUuEcAzSDG8rMmV4fPyI9XpF3/es1yVt36OCoq8CRgwwwjHIsrgY5aO6amh7lIhQl+8tTsRFnvhrEXHI7yqkNnR1Td/0DItBVPnsWkJvqNYV83KNSlMGakwoA6FN+favfsHl5y17wzFNVdFVHaEPNGXDZHqKF5q26xkNBtEAicDF/IJklJFUKUKqKF3iYweRJAlBSHQi2T/YR4iAcz1VaciLlIDn0x9+CgSSxDAcDlivVzhg72DEdD/OYdI85cXrMzoXbR4HScKoSBlmM54//5aPP3zG6zdnhCA4ODyJmL1UQBchTykhRJqwSgxt3dDUDSZJmO0fMtk/Zl21vLlaUKQDRL7P4w/HTA6e4FyPSzJuWkeS5HR9i5Ap+fSQv/ef/CO++vyv+eu/+DPKdclgC4NUNQqBtwGpDN26ZzgqWL6bU745I0uH/OTv/yG+FzgCk9EYoy2rtUX62MGBxwkPwmJU9Azvux4ZLFoK2rqmby1ZMkSbgLOeIDpMZsiHkmxt6ew1Tlqyoebg1PDt59ccTqf85Cc/RMSN2w3cEfjzP/9zXNdzdLjPDz76iL/8q7/G+hj4xWZRS3zf534bz8TGlngDL21jnRQy+rEHEecKRjEcDJlOx+R5FqFeBIlSFFlGXaQ0TRMH3AjevHrNl198wXg8JMsykiTZiWtxKZVwv0j+TY7fatC8ffJ7wZ7vBund4z6j57s/f9hV3A6kdzLbrcbH9vx8f6DffeF31fp9yOjhNd1//F12/W5W3Qrd3f1cyc08JdzdJ+LrWynt+HxREVaglURITRAKpUwMViHQ9x1dXTJva5Y3l8yvLmmqkr6pMYALgt7GYXSaaIxTqC7dDJIDQoEKW3qfx7UCHzxNt5ECTnUcbPYax8Y1futtEb1PMEKgnSQRkkQltK3ly599TtNY0nzMhx//gIPpLL6PKLyLsx3vPb11CC1wvmFdzrGuZTQecXh0wHg84up8Sdv2BC/xXqBU9HMQUtBbT5FpIJCmKUpNUTLgrCOb5ZGBQUKX9NjeUa07Do5mjGcpyQDeSx7xy69+xqpdgJO43tOLbiN37JGJQxqDkERLUSFwvsdax3AyZDQYUbUl1BD6jB4HqY5+AK1g6Ib88q+/4O1fXjLsJcJWeNmT+JrQleTDIW21ZN1a8jwu4sngmO5N+OGPfkiWZ9Rti3WwWpbkeR59LowheE/X1WRZitaSpraMRgVJovG+I8tzBIEkVTGZKUfTrnC+iZWjVjz74AmTg2POzi8pqyVaOhrlSbQkVXGH5P2nz0iyIUgDUiOVQmkJLTRtS1Uu0RKSRMVAJA1CJnS9JxsNmBZjmjbQrFomoykoz2CWUdfrKFshO9brdZTNbyWp1uyfvseXX3/B9PSUTw8PmU5nNFXL//t//J9o6gbpJYkUZMqQouibnkQIXv3iGx4/fsrsg1OU1CSigDwuf67L6EcRZ44Wxabz7SzBW4xWDJIC74hSJ/MWIXtU4jG5wvb9hm4Mi/UlTx7v0TQwnHgGY8OnP/yYJ88eR0o3Efo6e3fGn/yrPyFLEv7+H/193n//fV6+fM2rN282qq7qdmZ5G+xvdwXubIG5F04exB4CqY6JfzwaxkLR2ltlVCEESWIYFAVN3dA0LRAoqzVffPkrBsM4SN/f34++EJuBdwjbmYL/zjn/XcdvlRRi4rxbv/Ybu8td2Qbvbye79wJvTCYxSD/cJ3ioeBoH9vcZQTt3uH3uXQho+/2uJV2cKdwF7d3z7mb0W/gp+NsB9v37RaPt78xObquD7ffiduaye83OOdjIQW+3vIVWUWHRWbQQSDy2a+iqNa6tCbZD+p4kBKwPKCSDfECWJ0gjSDFIK2jrDrcxy+ldtGV03iG1RHoNIqpSti4qZ6appvfRDESF6ClshCITCSooCl1ga0e9bki8QCnD0/fe50effYYIIkprSxW9ICDS62wdpdJDoKzmBNFRDFOG45wgPOPJiNFoj7pqAYG1PcpoklTFpSUtkUTanVKGyWSf4XDEulyjVIrRRdS+8aCThLrusFcdRaeQiSVRBd4uEWhs7yltiVYKpeKMQUoDSiKTyBbr6obBqGAwyPHB0vsOFTR14wkqdlG+btGVYsSQ+u2a0zQnM5YkEWTTlMTNkf0K3+bowRDhLFoF6nKFVpJyWVOVaxbLBQHIijGD0ShuCW+0b5bL+SYhqOgvXW/0soQjyRLSNM6vnG8QBLSJ/sC9LXE+dsZJWpDmM957+pR6PWdx+ZauqQkyYLuWxbrk8HgUh/a935i1VIjgoiVm21KWFUpB2htMYhBSkxUjvPd01qNTST7IydMxwUWNnvVqTudgtr8P3mFthDbrVUPwDofgwx/9LqODffamY/K0YL1YY/K/ZLms0M4zGGRkqcJ1NUpI2t5ytbzg//F//W/4nf/47/DsRx+TTHKkUmSpoK7KqJHkAwKLkLHr8gLybKshVWBtILgevAQMrm/xWFQaTXSkhmJgWJWXrCtLUnj2Dof84LMPCcLFeYX32N7xJ3/8bzg/u0QiuPlozg8++owPPviQV2/fst0ifshwjJ9/2LJ+bpfYHsRUITa+9BKyNGE4KMizFO8C7XYLX+sNvKfIkpTBMMrZ931PmqbUVc3Lly+RUnJ0eMR0b0qSRChJKxWNzsKdAdpvcvxWgnhCydshI2womeq+cBxwG1jZVPVq83MlVRwcO3+7i7ANmvdUTwX3gvv2uW+fc6dT+XX00btOZmcwLnY0RnagptsuyN/pE+3OEbZPHx/jN7/MKGHrdrDECEPdQVK3zyNASI11AZy4ZQP44PBdR1utubm6YH55Tl+tEH2Na9b4tkWEQKYUw/GEvb0odGdxaGkIAdrW0dQtZd9wvbhhODEkqUQnkCQpzkHfOnSyqe57gRI6JoA0I5Ga0DoyYVBBUV0vacoGZwOu7pAmIdWKvu2isJeWqCCi1pFStE2DTqDvG4TqEdIx2SsYjFPqdonXnkQPaaomahr5QG9bxpMh48mQslrFPQMJIgjqumc8HuJ1wmpZI0gYDAp628dFICmxroPgubpY4KipuwrhNRJF13c0TcNwkDEcDkjyJDK/cHR9S+fjRzQdpKyqJbaNC0lG5mAFNgga3yG9YugUX/zic/qbBceZYppostyTTKC5+hqMIRtkaPb48OlTqsaxWLUs5mvqsuT6Jg4uP/3Rjzg8PUHp6A29Xq+oy4q6qkizGVIFBJ7xeEDbtWR5gpKeqlmQpCYGWd9jjEaogFbEXRcfaNuazlVk6ZAiyymOTzl/85z/+U/+mH/7F39BQPGT3/1Dfv/v/kNWVUuRFxR5wnox5/LiHVmWoNME7yxN11O3PXkxoO4iHbLtW5RUpEnG/v4hy0VJWa5Zrtf0fcPUj2IlKuNCamMtqVEgNHunT5g9OuHLX/4KpwLzukNmGdJEddmub+g7yHRMWK5x+D5Qt2v+4o8/5+DRh2TjEdoIluuO4DO8q+i6BoQiUQYfJEprBjpn0TU4H9BJgqg9ShikCtRdS/CetiqRRiATjdaezjZxTyfxPPvoPU4e7/PFVz/nsx/8HljH/GbJl19+Td87+rbjV19+zdHxE45PThmNRiwWccNaiLvltd154m08QkSjZWAr9iaEiHFRRGh3PB4xLAoInqapY3fgdYQcicVMlqXsqwlGK5q2I0kSiiJHBLg4O6dcrSmKYiOUp+/Fwq2K629y/HbwkfwuB3aXzvndzuB+VR7t7O5n1BDC7fD69mcA/n5lvnte8Wve/Ifnhftd2+79d3+J22u863juugrn7C1jIN4cr8PabvPcdyqxW5jLORcpnlsoS8mNCuKGHeAjc8JZS9uU3Fycc3l+RrNe4LvImNG2QQaLQCCDR/QttlqDUFjn8J1DbZhMGoNtPanSaJEQrI9iWMbgXKy+jUwJQqIEmzY7Y280pV1XrKslVV1j2x7fOdq2o+9cDDplx7fffs1of4/Do6MNNh7weFwIaK1AxKAFLUI5iqFBKkfdrhAoPFDWHbaPXaC1DW0faLsYAKy1DLKU1EjSRFNXaxY3cwAGWY5QmqoROBcYjye44BDSsjed8vXzX5KonEiAtvSuJ9UpSmqEB+/CBsPP6Xz8+xuORlGqoGqixEWqGE/HpOIA5zUBQbtsGPsU9JpRlpFZR1EITGLRwtL5mtX8gnxySDqYkuRLvEg3hvIzjg+PGA5HHG3M23scbdtgtGR/toefjLF9S56nGKMiA2wz+0F4em9x3hL6nixL6WqL63qkVFgbbSGFUNRNw3L5hrb1GOH53R9/yt7sgN/7gz/k3dklf/P5L/n881/yo9/9I372l3/N+x98wGw6YrmYbwgQikRr+j7y4ruuB2lwQXJ1s2K1WnB4JMiTCVVZYfue6+srhIDDwwOklLRNQ5pmSNmTFYMobCcNQSekmWb/0Xu0dUMwFc8++ojQdVy9eYmUAqUhhJ6+tXgrEMIQ0IxHewyLGUaOEFIyv/qGq+sbxmOJkFn8nCtNHIxoCAKTpaRJjveQpJrhYEheJNRdxtnlG9rGo1EIAmKzCCiVIkklyvZ41TA9GCCUpW0cv/zV51jXMxwNaLXmxctX/M3nP+f46ISw+dvve0f4DmT93eMWzZD3Z7HGGIaDnOEgqv82TU3fd5GQstFMihIaDkREZbI0xWhDmiaYxMTPINB13aZYuCtOnYtdxW6s+9uO33LQHNkqdwNgbr0Bti9899/va1ceQjBSyuhAtvvGPcDodh+7ueN3oJ/d8947FwK24nDiDtrx/rtw0O3tG8G+aHptN53M3Ywg7jIIgrXRv3WrUEjAhRAXsdr2lm6mjYnsqWAQG/5wcIGu7VjcLHjz8hXl/BJhO0TfQV+TSY9KBcHFpbJQO8quJZKxDWhNu3R03iNNgreCNBmSSI0NLbYPSBEoBmM+/uQTiuGAb198zc38mlRlGGtYvFtzfXZBs6rIZIIM4GxPby1N50EKZscTZkd7FMMEoT1BRChsay6ktdzQHD3OdiAsSQqIjrK+pixLBskeUqS3j5Wmp7M9l/MbmqoluECRHCBFgnUNb16+4urykiRJODw6IgjJdG+GEBohUgbDAuc7rq7PEJ2m7qqNd4RhUswoqyVYkN6QkGNbz6JcI4xmMpuigmG5XuE6j5YK6RUqSNq2IU0m+FbyZPoMsbS0yZzBaAz1mqVdM8kTmqahQ3Jxdo0ZLxju91xezxFmSJJmHB4eQQjUTYNJDMvlElSEJbIsQytF13qU0DjbAw6lNi5ewdNWTVwAFKC0pKzWABuMOeBswHsBAbrOcfbunC9++SXr5QJ8zwdPn/LRD37E/yabcHD8r/nyq2/RSvPR+x8wn8/pmpK+a0gTTdu21GXP5dUF63XFj378Y1wQeBQvX79kOb9hvSjJ0ylCpNR1y2Q6RghPYgQCj1IJXWsxSRbd0qwihJ6q91gRSIf71P0NV8uSs+trrPCkRYJ1Nd5LgoiFg1AqalB5RRCGvJiASFFC8frVBZ9/8XN+9OPHTMYZIMhlpGB7J7Bd4Mn7j0lNytX1NUJ5lLJUfUNVV+jEME4OsDjKpqIYFngaVmWJTiSr9RW/+OrP+OSj36XqboCE5y+/wvqG8WSIzVO0iQKLYsPUszYmBKVMTNQPYtDdjHQ7UxAbppSIc0cpKfKU8XiA1oquaymrEmf9RgepvwfVCxllMvIsu42dUse/G7tRStiePxal0cFx60vz/xM7zm0VfxecuWcwcw9v57tBeus3sL3ttjp3/v73cXp9+2KEvA9RPQzkD4/78NId/LM9xxZOuve6dq5fqTu+r3N+B0LyG90ibrcrEXFSK+QmIdro5eq93TALIk2ztx1+Y2yitcH7QNe21FVJU5e4tsPVNW25QnQtWa7IjMT3nq4LhOBxtkUogw0+WlJ6Sx8C0juyNCPIaOAjpIbgyZIhT06f8eTkGS4ElH/L/Lzksr6BxqKJxjDKKyzErsj3dM6jc8nJe4ecPHmEHg3QmcbhkNIgVLSRVCYyoJJEI41hvmrp+gbvK2QB9IL55TmjouJg7yRSZ5WgyA3InrJcsVzcMC5GDNJ9mvU1l2dnzC/eMskLfHCcv3qOyTLq9Zzj4/eYjPbZK2YYI1mc3bB4t44DVuMZTuKmaKqjA1y7bGOfouJcJZMp/dpT+xrbebQyiACud1x1V0z2NNVijl0qTk8f8/lf/4yL528ZD6Y4o3Gd4XVZo+WIrteY0Yzh+ITepyRmyOz4MWlaYPKE83evEVqxWC2pLkr2pgOSROP6WM0RiFTREOi6HiHCBi/3txLM3jukjYWIUpK6bnEObOcRIkFJw2pZMS6G9E3D1eUVn3/+K5RMODw45Pj0Kf/4n+7x45+cYduearWiXK5YLi2Hh/vszfbwrufNmyuKwYjZ/hF10zMYZAihGQ73ePfmgtk0qsbWddQbQgpMohHC410gyXK6usc6hzGaLDPRgD7R9N7TNB1TlSHNN9wslwwHGaNkRn1zjkDS2UDvBFYovI7Lk3sHR6gkQylN31kSk7B/uEfnLPPVkqLQeBkNZ7Qx5EWCMoHGrfCqxgwtXVOyqmuqpot0z2yEVikDlTIYZPT+mhBWsXDLHG8uv6Ru13z05HeYDk4pm0uKoeLo4JCD/UNGwwmPHz9ltVjz9NlTfvXLL+j8XdzajS/fF5NuY2KIC7tZljEcjUiTlK5toqti22JMipJbVWl/u5sgiDpG6WYfiSA3PvR+83cTuwJr7Wa+uzHs2sS3uq7/lggfj984KWxhni02f/ti1X1O/sMWZbei3+UC3S6XCXGPTSR2Ese9oL8zAH7IPvp1LVvMyPd/UQ9/abuspYcdz9b+Lm5TE9u3IHf+CO5MhbbXtb1NKRUrP+fpbU/TWKQDbTw+jVK33kWFTi0EfQh0TY3talIRSCUYCV4GgtwyGsCGEBOMB6U1QinsphIRKLTSSG2wrmc8mvLo9D20zDBC8tGzT1hcr3j59XO00wQfInNIaVSaEDwoLUjTwOhgwOHjE5yCtq0Yuw4tDFKJuIAlonZVhAA0QUi6Lu5aWNdSt4JECnrXUjdrynqO7V0covYST0dVrWnqGu063rzoWF7f0FY1hTEMUo0PHi0UQnnOXn/L8mrB+++l5LLAJQpaxdWbOVW9wLqe0Z7h4GiIV45cZyQixVaBtquZr5aYvGQ4HaMTQ9tGFVjvHEZrTOLo29cIm3OQPuX69TVf/PwbhLMcfPCI0fAE2zbUy4qD8RGolPHhEaOjJ3QiJx8dkg73kFLGzfMkQfie9WpO2zVcXqwYjwbk+QApFVobQnA0bRO7Lgld19zuLvR9FDaUMm7MhuDp2g7noKktzkqSJKfvLEWiOT444OryitWyZL2uSbKK5y/f8MGz9zk5PqUuS6rVmv3ZjDcXbzFGc3x8zHq1Yn+/5ejoCB+g6ywnp49BamYHRzw6eUKRF0gVceq2b5HaE6wgTRXOsikUJOvFNVrDe+89IRBoe4ckIDQkUvPxp58h+zXlxUuuXnyJTbINlOKQRuKcpHWezoPJo6f45fwSlUhs36C0ZLleMB7l5IMxykTfa4enyBKW1Q3WdagEbFuzrC5ZNx0hGBApOjFYLxmPx+hEwWoRdYxsjzCetp9zswz82V+s+Oyjn/L0w0e8e3nBZK9AmcB0NkAaz+xgj6dP3+Prr77BWn9bZD6sxO91DduZqfMIKUiMYTwakqYJ1lnKstosi24eyx3S4lz0eHa9vbXwjbsIYSN1syUUBsTG/zlslidFCHRdlM1o6+Z74+TD47eaKQgVdWW2gdWHcFs57y6GxSB5xyRiEywJ4Z7Y3d2cIFZDMYluEoy/22jeUlTZ5gtx/w1/CAXdmyuIbULa0me3ycKx27VsDyHuBPaM0Wgt/z+s/WevZFl63wv+ltk+3Ilj8qSvLNu+m6RISuKLEYQBBhh91gEGGN0Xo4sxmpHmcsRL0yTbVHeZrLTHh9ux3XL3xYpjMqv6skvQBhJ5TMSOHeY8az3P3+Hcbk53x/iPnaIxmt5ddxJx7CQIaC3RiF2IRsAMlrZr0MZgTcxW9rbHmR7T95ihw5kBuYNYrl8XHyByThO8lHS9YUAQEomVEGRAphLjLGUxQmtJ2zas1w0yLDjYWzDO99CpYm804xc//jmPD+7RrqMwzA49iZbkSfTS722HyiS6TJGpYtu3lOWMcTFGqQSFItUJoypjvVlDsARrML7DGRPtLHxKvd5ibYO30A8dzRBTqvDRyCx4gxJQkFEv1/QXlwjv0SKQiEC3vOTs7IK8KNhuW5RO6LYQjrZ025p6FdifHvPhkx/zN//tr7HGUElBY1bMJjluuyIoh8w1rrcoG2gWDcuTGq+gM4E0i66reZYzn89xrsH3lo8+GjFPDvnJz35B1wysVleM9w84OJqyN5nzzZcv8A6y8TGt0cyO7lGN98BHoztrok2zkgUqURjTYeolwVv6bkNVlQh5bYscyIsKpROcBWscdd3w/PnXnJy8YboD40/evkEIwdOnz3hw/zGpLvDOkukEbzz7ewfgf8ticcFydUleZpycvOZ//k//if/wf/4P3L93yE9+9mMGN/DsRx8SfCwWlo4km9BZQVEU3D/exwwOJaJX/3S/YrvesLy6ZDabIDRx4R9cpLgiMc4xWIv1AdsbejMQhCQIGUe3Ib7fB/Mj/KOn/PUX/8jp6WtkD15myCxFjBIWizVGepLpnMcffkxQimo8ou6uWNYXdGZLlcN0f8JkNuZgb8psOsEMQxR4bnu6IWpO/OCRjWZ7vsELz/0nDxnNDlhv6hiN6h1a77IQRPws+GBwZgV64PNv/v+U2R6zY40PDdmoRKS7TZNOkbvFW2uFc1ErdF2zrjej16SYEMKNLX4QsaCnWUZRloQQ6NroT+Wd2dnRWKQx75BtlNI4ayOe6D3eX2/AI0bpbPRtssbjXewcikIjUFjT0fcWa/8Hj4+uRWlZlkVwqe9xJsq93xn93BTpcGcXfQ3mEufRdxeK6wXiukCzWwjCu91DBHLvms9xU9DvJqW9j2tExpi7uYaYbCTjWCie/Z1rj4IegVLixpckjoP87s1RSCl2mEEAYe4sDAIlIEs1aZA3iUdaprTB03uL8wbXG7RTBG+xQ0vwFm8G8NzQVgMShyTsugGVFvggKUaSRARaZxjcQBAeGxxllSOCZb1u2a5bpNBkoqBZbGnmW8bjMobjpBo7rlDeEbxlPq1Q0jP0W3QY0Ls/ZBss3XbAWUexV5GEDOk1ha4QNtDbLcH1O1XvteeKxLQe4wOKBJUKVKUj88IPMAzYrgcbnWuyoNBGYrcG6zvGVcmnH32IGwZO354w1Qm27am8IAlRXHUwSXHDml/9+msODx4zG9/jw8efcvbmK1hvKLxgLAyptAxhIGSKsUqpkpJWSHoNPT2jUbRLmE5mLFdrTt+eM51OEN7z5uQNkw+O+eTHPyARFUqkaKVYrZbUfUt5cMgXX3xF++otH336E2b7Bzjv8LanHyxSOIS1BOfxTpDnY0aJJtgtTb/BBcHQbxFCk+UTjLFcLbaMRlOOjvY52LfgJMF63p685vTkFdb2HB0fcnbyinq1ZL53SAiS/fk9pqM53sNsNokus/0z+r7i8GjKanPB//M//yd+/OMf8+TJY/KqQMucohiTaMWEiqzsycsMKT0WhUpzgtOkSYHWgrbvmcxnqETgXB87QmdI9CiORztDolNm8ymJknSDQSY6jja8vNlMuLZBIzFdTzXKGaxh2wSGYJFFgp1NmR/f5969Zxw+/IB+8MhM8fzVl2zNijQVTCYl02lFVij2DsaURcrVxYp1fcXQG5z1NOst/Yln88Jx+bqBqmA2DQjdY31gu14BhrZrUDJmh8tUUlYa7wLddo0LPbVryIs5iRijS0FeZpRVBTbqfo6Oj0iSJILzQNN0bNabWLBFVDkHIWBnySE8N9MEnSQ4An3T0HUt1sVscYIkWItUirZtd/VNIYSJoyHnGQZL2/Rcxx7H9ES3U1VfT1kkYogLi/UwGIux35F5/x3HH70oOOd2/jK3xf4alL17XAPFNxf23sz/5onfrKI+9s7hdpWFd8dL7zObbh/nX8YWYp8RInrPbZdyKwW/q8SOt7/ucmJRd7cgDm7HpY+7BOdB7q47UnUhhCjOgkgbNDYqR/OQIaXCGEPXdRjTgfcMQ0fT1jhnKIoM31kSERDXIyGl0CIhLUoynWOEwARPvbqKFsyJJlXxf28dRZ4zKUbsTfZ4+vQZ4/EYISzb9YLBDjHvwBu82eBMR8hKdCqxbovva1AC24NIcvK04NnTD6mmBxTFGKViSE1veoztUCqQ5ZFpFIxGywy8JhUV2SjByxjLKBU02zV904M1aBSJCwzbLaZpKMgRIjBOMu7PD9gsF7yqt6TBI5whEQLhesIQ2C7O+fQHv+B8fE4YWp7eO+LB7E/55d82nL1csV/lzDKBJmAd1LWltRbywN7enC4EjIJNu8Zi6PpLRkmG1il21aOUZHl+yVnxhiKbUeX7TCeHJHmBWa7Zdh5rFEKlCCUoRlnUhqDwxiGCRYsAPpCojCSNATAyaPAamSqMbZBSkecVQui400ST5xVVNcFby9MnH/L0yRNev/mGq8U5680Vn336CUII6nrL8+cv+d3vvkCqhPv3H6ISwfxwyq9+d8GXX33OaFKyt3fAsw8/QErNpl7z4uULnPBM5nscHz8m0RPyYsRkMo80SBmNJrM0R8kcQQLCMp6MWa0XrNcLhPBY2yJFYL0eyJIEaz3OD0zHE4QnzsbdQF5VJEmxcwYeyAQMzuGQlKM9hqZGqIxHzx6iZhWHWtJayaMnz0iSFCkCSsLLF19izJbxLCHJNaPJmNn+mLpvadot9WqNbQyuHqiv1py8PMVtcsbJEdInOCMQQTB0PetmyXq7IEkFQnp8iEB/kqQ44+LGLMgdnTUnkTmJLGkbR1d5XCnYrNYMpuPR4/tU5QiILK5hMHzxxZe8ef02UvVvOv7rzW0AcRs1UNdxfBrcsNssS6wNcZM2DHHEfKcGuuAxgwP6mxqJvM13v8VP4/jamshWcy7intcspX/p+F5As/eetm1v0oKUUtHF1N+CGbHgCtQ1XWx33FXYvX9IISIH/e6OnTsdwl2cQnz7HHeB4ruK5usF6toYKu7mo3T8mpkUuEXlJeId3cVdvMEYg/NxjJQkOw4wtxiEENdCk2sBnadtO5y3gEVISNPkZiEc+o7BGqw1mL7DW0OVKrxSJDIgpUVJgVSaNC9JqxEeTb1es2hqVnVNMSnxLpDnOT/44Q+Zjiv6eotwliJJkMFhtmeABBU/HOu6JpGWSSGQuQK7RiEYKYvKHL31VHlJ0Cmf/OgnPPnwU9AlHslgDYPtobPUTUte6Gjr4Dr6vqXKxhR5AdIw+JbTxQleGmbzEUVWsHULfD9gDZit4XA8Z7o/YnW2QArB+uyc//v/9f8WaZt7M44eHXF6esJ2s0JqhRl63vz+NxxN5vzpp89Iiwnr1YrTeslItdgS7s0001KgnMJ2jtw7OgmbroXFAqUEneuYVRqhE7wJ+KZHpqAn+4wmB3TG8+bVcwYDSlXM5/d59OBDvFeYwVKkI378gx+AHEj0gJADzsWRgVYq2ml0LaNyxLiaRoGSsDTtilRIlM5QiUQIxXLZkOiKD54+pRpNIoU2TRiP4fLqnLKaMZ3tYW1/8xkaT/ZJ0hEfPPuUvZ3KfDofUc1+zuMP7/O7zz/n9dvX7O0fcP/BfepNs+vwQWrJYFoCjizX0ee/d+R5HGVoJdEqiywgkeBDezOmGIYBQRR/WWfQKgZHJakky6Jx23bdcrW4ojU9R8f3KCromx4ZoGtrvn71motNS5lo9OyA43tP+Yt/91foccn/5T/+T3g0Tx5/iJKSNIWgLEniqUqJTD0qCTgM682GbltT6Iywtdi15er5JVcvzxnWQ8TxMkXbDkzHMw5mc1wa2GyXtPWWVlmqcUaSCAQS6aPmRzhNLipkqHAmJ83GlOk+fshYX7V06xO6piNJNaNxycHBPkJI+n6gLKud+A82m5pNvd3RiHeGdIIbOmqcNJiY3Swk4Ak+LhrWulgr5Z0Rexyg3JBvbmqyg+tAH7gem8uobdmZO16PtGO03b98fK84TrkLc7DW3gLOd+ijdxk/EeB9/xy3o6TrcyLeHfu88zveBYavX5xrj6L32U5wq46+211E+ljUGkRhxy1b6tpM6/3z3HYQavfCRt9859wdIcg1dnKdeBaDuZXeJR5p+U7WxPVYTalr0Mnt8l3BO4uzDhVNbsF5vHCkWUGe5yAEddPStD1CKMbVmN4YVssVEkmVFwRrSVVABkO/uQQ7RJFUb2h6y+Ali3XNvcMJTx9NmVUVzbpHYcikQBiLRrDtGu4/fcwnH32CVxnbziLThERH/55EssvU1ZiuwwVHnpTkaaTWNd2Gtxev6bceg6HdnhB8jzQO6QRqcGivsKsGmwa023HwlaLSGi0Fdrtlc+FRpqXUEDBIAd36gr////w/mO/fZzTZY+h7kiTw4b0x22zKJA88fTDHdQ2Lsyv04GgjAYluaCHAWMEITbBxaLlaNRhhgDF7R2N8rhiAOgwMtuPi8i2pzsjlmFylJD4Qui0PH02pKs22vsKrAlQSg4aco+160qQg33UhQfqdbqSj3i6ij5Gz5NmE8WiPPBuRJTkyiwlyIiuZz49o2pavn3/DZBLV7MZZ0kSgk4LeNDtzP0FnetCCB48eIhNFvd4ShOTx06fUqzpafOAZ7VV88zYGGU2kw7oehKZtO8ajSVzUgyR4hVQab+LMPEsLRtWEzeYSNxjybJcMKMTu8+zp+5au2cY4WTuwXFxinQEvMINh2NaoouDTn/yCfrvi898/5ze//Cfmn3zC048/Yja7j3ECneQoJUlzwdnyHOM2TPcKRN7i/MDi6oJcR8PGiR7z+vQF51+/pTupKVxK4TTLweO05GA+56OPPyIXGa3pKHSOFpJuGLBDDL/xuzyDVMfcbkyCbaNlhxApRTVBVyM2q4FmtUHgmc4m7M2nJEkaa52K9NCHjx8yno45O7vgiy++4vTkdOcdeR2qpZFC3moHdsmLkakIWipccIQgbjaz12y0WP/e1VeFEFeL67TKeLgYGbBzcxZBIoKHYP+oWv+92EfX4PJdwVde5DeiibsA710GTwSM393d37KMdkZ2dx7n+v/3WUPXWoD3XVLvHt8lojPGMAzDzXVrHW4Uf9dLuBC7LII7FDMpdQSh0oQQsl1htzedkVK3C5TzDmzUFAwmepM4Z1FK7LxsHIM3uB1V1VlDCA4pop5AeIcdLJmSiGAJ3uCNx3Qt26AwdDQ2ctXzPGOwBrNecDib8/jBA1yzoapShn5NvznHbJe4oY07k97SrXu2g+D+vQfcO5iBa+maBm9qghsiCGw9qS6oDg748NmHpFnJ1bqj7S2ubUhSSb1d0A0NVZmjlGDoOpCRCZOkGVU5IliF6U7AJghhMV2DCJYEiTaSxEkmOofWsl4tyNNApiQqhBjG7gQyCOqLhjzTCO9jbGGAVPdxFLgNWLfGDoa6XbE3zXm4lyF9x8ODPXxfkQyOpasRrkVLRR8kMs0Yz2cgBWmW0bUtSWO52hj28ylTXeKUwihNmQlQCXk2wg8W0S8ZFVOSYGg25/zub3/FoFJsNubTX/wFSTnFCUWe5Uxne4zLMWmeA5GRlGcZzqUgBIMJKJUw3zsk0yXOCfreoFRkHiVphpCee8ePmUz3qJsNOlFRz0BApw6Z5KxXK2QCSRa706vzc4TSlOMJbWdIEsWq3tC2W4oiJZtoerPm1cmXFFXFuNJonbBtBrqhpygqlE7xDrx1u06gIkkzkjRBaUHf1fhg6AdL8DumlIBcZ0BASQHes1xcMZie0WhM27ZstzVHDx/x9IOnCAHPfrrkyxevuff4GWeXG6rRnGfPPkJpTTkuCGrDb7/8JwZq0jSq9CUeYSy5zBnbhMvPX3P5+RuoA9mQIY3C9g6sRauU2WyKtPCP/+0f8AkkkwRpFTiJ6SxyVJAlGVppetcROoOvA/3S0G57aMYUqiXLMkw3gBcURU6SC6wdWK+XhCDIsxJBBOv354fszQ65vFhydblA4AguvkZFkaGUpmv7HQ7g3zHtDAiEkNHEb8d6vK6NcqdTuE6flEIgkaibfObrWhbPJJXcLdi7TuQPsDTfP74H+0jvVqs4NhFCoqS+ycMVRG/z68HWjdPpNV1o1zrdTNh2eMS3mUi3xfxuzOfNIvNemht8m31097j2LYq5Af7mnFJGUOZ69HMNbAvxrpeJUhFcv75OY287i3ib61U6mrg57A44j7hABMgFSoqdYjRmADtv8d4iQqTtKTxaChQB4S1id81d0+Jah1M5QeekWY6UAkvg4eEBxw+O2J9PKFOHH7Zcnb9kmkM1LzGtZ7vdkkrJbHIIyYjHzz6lGmecnHxFs74A50mEQOqEVAmyasaTT37E4f1HXFytOF/UUZQkAnInturalrxIMMHGYKAgMAZGIo1zaFLcILC9oLfxNlU+Zq8oGFWKdAC1ddjQ0dqeNLV44xHeRZvrAL4PaBXwfXxvtRQoGUBYRmXKqEqYTkb0Xcfr1+e49ZbeafIk8NWvP2dcxZGVGQdaY/BDbM/LMuMv/vTP+Orr55yfnWP6jkorhtTSL1f4uiEfT0iTDK8TBgeZA6XA9TX95RX1tub01XPqZsXGK378l/8HUm/p6g3VZC+y1mRCkJLODBTlCOEi/dcHQZZXHBzo6J4ZbseskdIb6PsBpQdUohBCszc/ZDzbQ2lJ13fUmzUST1lqhEq4WJxTdzXODVhjabYNZrBMxoH5XkZvDZ1taVcrOrGEbODlyReU44oPn6S0piMITd1sSLOcMpdxE2OHXVecYqzHOUEQGhckfW8ZVQVtv6Vp+l3RbNEucH52Rmt6BmfYdlsQAes8MlUELQlJVJx/+OkDPvnJn7BtWk5PfwVecHR4hAsGKwfOl695efacfJzgaMkQZEJTBoU93fDq/ITTL06QnaJMRgiXMQwOnY+4N5kwvnfIdG/O+fk5i5fnNKFndDQh2y+ieaIligg1OLv7O/SeflUT6oTcjwn9QF/XbFYdUmVMqillmSJ0dLUFwTAMeCd3ZnSOkAtCiIu8c/HvOuwMH4uiwFl3U6si4SXciGP9jeFfJLhoHYOubqx4uK6vu2z3EPPrIw1+97Pd2FwqcfP99Qjwjzm+B6YQL1ruCqh3AYuj3mxvxBJyt2JFZa/f+YXfED93uxx2s7Pvdki9+/31cRd0vj7Hd93+u+5/fR+l5E7h5+94LYUbMPyGKYUEEV+8CCrHnVQcIwn6ISoNo4Jx92aqGK0Zx1L+hjIGYI2NuIEZsNZE7EFJRKJxffw6zzRCSxLh8aZHeIPA7UZzPrKTcFjfkzFCS/jwyUMmkxIpPKV0ZK7h8vwFzcVrDh4coL2iaWtM15EVY5IiZTrf597+lPH+lMXqLV2bR+2IDMz35qw3DU5mLOoOtayprcTvMJCyzEgzRVEdcHC0j/Mm0mFV9K3pB4t3cHm5wDjPk0fPmO/P+ObF5yg18PBozqwoGK5WyOBQqcOHlFEpCWEbPYgAnMP1Q0zHkgJrBoSAYQjoBNIMJpWizDw6NHjfMk5dXFS6HmtgVQ8EI5nOM3SWIRJFsBYEdEPDP/7yH6i3LV3bY/ouirJCoG/WiL6lnM3xQmO9ptAJw9Bhu5qrk2+g25Aph2vOKGQKKqFUgiQEVJaSyGh0qHQMpOrsgLQDUkQmmxISa3oEEuccRVGihEbKwHbbIUSIthc4mjbaHbRd/Ny0XQTC+yGm7qVZwng643K1wlpDklQ4r/AMpEUWI02DxTKwqq8Q0mAaKHJFa1f8/vk/kJcZo/wI0yYU2Yxu2CJ12GUmO6p8jgsJ3bJhs93QtE3clPjAYrUmy1LmB/fYbrf0tuHs7IzF5SXzwwOWVxse7j9ACEE1KiKwanqUTmm6jmw0JVUKZ6Ll+PG9Y8Ah00AXaj7/+p9pbU3menLtyGVBGRLakzX1iyv81UC6gW5tWLMmySvy2ZQnn3zMdD4nzTNMZ3m5WJOaQG8E28uWbFKAU+hCIUVKouK0IhEKFQSZCzAMSGFIhcc0Kwwpo0lCknikdJRVyWhckaYJV1dLtnXEWpumQwpNXdcYY0iSDBEMSiYURYGUgvV2izFuV3d2BXVXsJ2PYVU6kSid72jxsUz74PHhjlcbnlQnaHGXuhoznWPEZyQ5RBKM3NHw/+Xje2EKgnfHO97vOgPenf3HlUzz/s49NhHXjKD39QHfdlX9LjGZkBFEuQsu373duwsDuxc0WtBG2wnJrbXtNc00ufP4u/xVDw63wyHiSl0URXQFHQbariUEu8MSIE31DZDsXHQqjeyjfsdHtzeLk1Mi8lVU9PoZlQWqLchCH6mbO1dVgYgc7xCDyr2CdrumKveZTwskA4fzCbbfsDp5Tnv5mnEC3WZNX4P3MJrso/OKxgTqtuWLr75EvFQsVwse3n9AohWX5yfURhLSETofIZKMbdeiiyn71RghPEmmohmeisFKzluCjK1sWaZRBOTijtB6hxB71NuK7WrB3rTkYDZie3FBpiagBmb3Jti2oWuucDYhSWOUZypB3WEddduWdtsSpIk7OQXG9AwoEgTCDWTCRzV32KVvelguNgxB4ERg2xuMh6zQWOtZLpd0vWVbt7t8A0EioOtq1mev2JuMmRQlm27AO0u/uqStr5C+xrgF7dCQljC4QJIkjKcjvDfRSt15irzEhYB1A0FC4lNSPJKAFA7v4uiQIEkThR0MOknRCbRtzKlWOsMOA1WRY42lM5ZNXeN3mF6apTgXCMZTVTOsl/RdAyKhGk9xzlDXG+Z6Qj5S9KdLxuOMk8ULyp2tRLfZ8OJkzOP7oOSM/ckBQhoWm1OqKiMrFFt7gZApy/qEy/VbRqMc7yyt2WCGARtyqnEJChyet+enbFYbnFRkeYXzAusCQ13z8vVLlEpI9YK67vj65XPa7Yaf//QXCOHR2sexWBl4dfE1J+ffkGUC2zdkWUFpE9rzDc3JiqxRlKEiLzIW9Zp1M1BNMz74+EM+/NGndF1LvViwPl8wEoL9NMcOA5u6J7SWJE/x1iODQiLZNlvUENADzMuKtul3jgZbglMcHT8hrwqU8Fi7pW8dWVZQZBotPU29Yhgci6sVX4jPGQbHZrmIvmVCkOU5RVFgzM5LTV7Xq8B1WJdU0V7/mg6vtEIQNVUBdiLHXRcgFUmSIwWkSQzmqaqK8Xi8+7qM3khJEs/1RzqkwvddFG45T7vKzQ3B6C7r59on6fp+10X9HSyAa0ro9Ym+fdyAwe8B2d91be+PoK4fRymFUtmN4vCay3ttWBcXFb8rvP4GhA6EmDC2uz4pxW7l3VnZakU/tAxDfxO0fb3yX7/hwzDQ990OnJZxnGEMQ+8ROiFkKU5H1WGiJLlICFbhbJwBmrB7XYXH4/AODuYTjo/2wLbkKWRyoG+u6Dfn6NCTFAXGBqTOCDqBZMJACqlE5iVCqbg7lQpIybKSxx9MEUqidEKQCTLNkToHBQKD1GI3009wXtD1A0Hu5pS710eowGCGndWBIhDYn+3z5P4zvv7db3j9+TfsjSpC1/PRB0/57ONP+eXf/x1bm+JNSZ4GhOiRDATpKLOElICUKVJClqcRo0k8/RAIdmDY9gjrokpUQJJIht5jPBgT2JxtGLxHKMjLqGRtW8vVZYP3ardDN2hFtEGQgWF9wbA+YzYbk4vA+XIdc7VVh8w83nqW9TY+Bo68nJJMK2QWd+YYQ5akqFRjnKXpO9pug9QZMgQclqGtCSLiWk2zRsqEYF2cmZvAarmiLMfMppOdO2qK0FHc5ZxlMpsy25uyWl+htOLhwz3U2SmXV2ckWiC1p66XLNYdi/oMVTpE0dOFDUas2PQWrVPSdMLF+iUIxSh/gNIJ3ggWyyuODmbM98comdF2hj6syUaW5fYNZuhJSoXQgVV9iT1tCQiMsRjhsAIWmy0fHhwT0Lx5e8o3L75EJ4LDwyNCgNG4AhzLdcOb0xcoqdj2jjwIhLN8/vtf0g0rgm3IgdxI3KbHnrdM9YzZ3piwdUiruLysCUowmo548OCYVEssBtetuXzzHLO1TFLFkKX0XUcCiDShddud6ZwieInpBtyypXAV2hqCCRihKKcTyjxamLug6dqO3tSoRqFkivc9zsUNRtutubxYIoQiLwpms3HcvacJRVbQti2bzYZh6G/rV5QGkeUpeZ4QdiwhIa5rJiipdypnjVKaLMsZjUfs7+1xdHjA0b0jZtMZRVneBO7oOwuB0t/epP+h43sDzeFOwb0rPrs7gkHEAJZ3i/i7DCHBrV2G510zqdt5m+NuJxAL5O26dBdTuPv93XPdFdJdgzG3+MG1rWxkEfkQBVh3ba/dbsZ3/QYpJbi2v5CqQGuJtdE0r+1auPZitzZiB87esJWi+nHHUtIKp2JhHfqOrt0iMGgbRyeBmMh27Urq7AA6ocgTqkKjhCERgddf/4ZmdUHiGoRzCARJVjE9eMDevcfoYkLvArrIQEVZvAaEtWilooWCFKgkQWhFmhUgJP3QI5wlLwuQmtFsgpQaYz1SQ9105HmBThKSLI5KrHe02xWDVRFHSTIeHj1gogu6zQbbtTz/6gtOzpZsm3/mi6++IdUFh/N7HN6bslm+pN2eMKkShHZ4Z1EpVCqjyCp8kBjrOXn9lstly6SQTPI4rtGKHS3S0Dm/C1EPECJOkyqJHUzUSriAFgqPp+sDIkCeC3IvUMrSr0+4fOtQ1QjBQFpIhq5n1W0Z8NiqwCJxOic92kfORohRgR8UaZISCIyqEqSgP4903YJA8HbHVOtQSuC9ZbnoSbOC6XQGwqF0QCjP27cvePToA8zgmc+PUGimkylVNaKoMpDQdQU2xNHteDJhb3+KMQ0XV2/ZP9rDsmG9PaNMPCGp6cKGbGIRafS4ItUkpWUIa06vHK/enKDJGfqWL77umE5zHj9+gvPQNDXr9ZJXL1+gtWRUVGgd/ZekngCKl2/eMD2Y83/8P/0Hlpc1r1+doFRGnpU8efyEvNBYa0izgmGw6Exy/+m96BArHJu+4eqbNyzqE755/TvGFdh2y6jIGc5rmrctY12QpIpeRL1Ts21waYImzvivTt/S1JdUVYIKNb5f4ZoeMQD9QCog04IkVTSdj59zkWCHEAFoIzFNRy408/mUhRdUVUbf13QGlMwik6odCIHYFTqL0lF/NB4lKDmhaw1pmrC3N6WsJnF0vVMUV1UZM0WUQoq4ICSJJMtTkkTRDzvSDip2lEIgtUCrhOl0j729OfcfPODp06fs7U0ZVSVlWd6xxo64qNIxvMpZ/86G+V86vt+icOf7m3GLEO8sCHGsFJN77+7w3y/W3LG1iIj6uxLs6539rfbhllUUUfk/YGvxzjVfLyx3GUzxdlKmd3AKtesUvpveen3Nsfhz0zUoHcPWo/GUwdohtsr9QAieRGvyPLtZXPqh24mWcpwZGBqBdz7ezwx4Gc30wg6gVlJhg2DoDUFqDg72GVUFqQokwoHtsN2aYGp86CmKFLRmcviAe48+xicjDHE+7aWiHzp8MCTOkjhP03UY52iGnqbrGE+nfPzJpxhrEd5SZgntdo0IOc4V0QJcCJRWlOUIoRQ+QL2NwqfFasHrNy8ZT0rKNMOJlCotmBQj2sWG3/76C7QWrOuOoBI++9mfMC2PGJdjphPF8y8s2+aSwfVIOgoVyAuNQjOfTggkXF71jCb7vHnxJuoQjqKuo0g1SSoROsGGAa0EQhdYD23X4K2gXm9pG4cZBHbYAoEilWS5JElCxDFSDb5hvXgJXUGoSgan2bqOtesZpEBPZqAVMs3Y/+gpcjrGJykQM7O3TYNOJBdX57x4/YL7x8c4CWJX/ASWay65t55lUyOlpyjziBHlmj5T/ObX/4Rzkr35FVlaUZQj+iQhiJjJ7fy1WZok0wkqEaRJiZAHdMOGw6M525cnqNQh0gEtDFI7Bob4Nyd6OrPBDhlVWpLqgun4AGM61qtTOrvmd1/+A73Z+VR1HdYMOGs5v4hZzo8ePMYJTUDSm5p7H/2IajymKvfZ33/I4vKC0aSk70es1me0fUxdU0lCaxoG15JpTZFntF3N51/9M+vmnDwPhN5RiYz2vKbymqmsCL1k3fSMJhVJXmIGgfACu17RWcNmtSCVUxwdoa+ZlJp2MNi+hxAoS0GWa1yqEV1gGDqkhL4D2QaqoFDAbDxmNt9HDJbeG1aLc9AlSUpMMewMzlraZsA6T55CKwyTScbebEyz7VEyYbY3Jc3HbLc9bdOQZgnT2QSlBM5HRlKiZLQQJ2JF1xtNpQRpVlCNJ8znBxwcHPHBBx9xeHDIaDze5TJH14ZrzdQ17V8g0TrZ7YDd7ud/XK3/HjYXu0LLrQAjhEjhvK7zt9oEyXXK2u2/d3fv1/SpiLR/V0F/t2u4+V7e2l3cVSf/oftGUHt3nnA9l5O7uZy8Acav53Xx/u8yjG50F/LWj8k4hwsR2JFCkOiMTCfRD4UddhDizvzawhYMMk12KWMCFwR2MAhryRVosdMsINCA9DAMBoegnEw4Oj5iPqtIRY8wa9r6HNOdo+RAliV4JFJr8kzRtytWF6eRNiM9a9MymBalBMoZEh8ww0DbD7R9j0OghxmbccK2Mxzdf0Qq4GKxolvUtI1h72APmaboskQnilVdI2T0j5Eu8syazYL1xXOE9RzuHfH4/lOyfMR4UlFUBUjFv/rLf8Nkts8wOEQokIBLLIePP2W1OaHv3jLOEoRvEB60ANyAkJ6y7ND3c2DOxekCkScURUqqPUpYEqmwu7msU9A6hxcSQ8p62zFYhXWx45QCqkKQpYEsEejgEcIi0oRBONbtOlqT5AWLwXAZLD5P0KUioElVwfTwIUFn9CZQFBVaaLZdy9//8m/49a/+gSdP7/Pw3gTvZfSGyhKUCjv8oI0cdVtzebbl/vH96A1lB6pU4boWnY6w/cD+4TE6TRlcx3Y1kKTJjdvqOC/I8hRjLGU5IREZo3yKALJkjBBbkiQjSEPrW1yIecAQGEzP3mzK4fQD6GcMnUCGlL09yWhyxGZ7xjcvvsI6w6uXr9ls4gI2nVZUIef1ac/bs1dk6YR795/w9PETghEkSYFUFi8SZJohheTizQkhGDabBakumEznFGqK6bcMZsO2uSBNAglE+5ONY2glfimZz/YZ2o5tY5jff8KTn/wZxXhK2xts33J5+oqr01e8XrYMQqPPVswzGBV5tOOxLUFAOc/JqoQh0aRpRmNbjPPIIFDBoXVAZwqVRbal8AI3ONIkowsOx4AUiiwZY4c1g+0j5pbAZBJNJrMsZTTKKcsxe/MDpCqo645mm6O1YrMZ8+bNK1brFYIYnWrdEN1gQqTSpnnB3t4+9x884v6DxxweHTPfm5MXJVoluzG1jBoIeZ23fl1qo/7K4wkiGhLueEt/1PG9FM3vF10AqRTvdiVxpn4t/oJ3d9/vYgO7LiDcYhB3H+P6du93AXcxhPeP9x9TRK35TdchpLxJgnv3udyZS3F7e0TEUa6BmmtabTxnXO3xsSso0hSlFGmVgAy0fY83cUHVOiCDx4eA8dGHxAwWZ3oSHAiDChahIISYZRx8tACpJiMO7h+RZQpci3ANoVsRhhWZNAgdk8SGQWCd4ezkFUG8xXmP0oGAAWHjQi0EBIvfhQdJL5imBR6F2V5y8epLNq1jvVhihWY82WN+/3G0LFCwHTYgWiyC1lis8YzKMU3TsDebcLg35s3XL0iVJsHw+sWXHBzcoyoqHjy+x6tX51gvaXuQssJ6EdPFEkE+eUjQh7w5fU5+T6H7niHUPDo6AjuADiS5wEnN7PiA6eEx3WZNlgU0HbZZ021b0jRH6oRF02O9p3OB/dGMz37+A2wQ/P3f/R3rRWRB6Uwgk0hblgK8FBhv2VjD2geMzjDWcTUMJAdzFqbGa4v2mlE1ZzY/ZrCSVGjSPCNRGqMcQQ3M90se3t8jTyzOxwV5XW+YzMYY27HdLllcXjGdzkjSlKvzV6yullxdrTg5OePTH/yMJ88+wYuM0d4cD9jgaJot69UKb2Nk5H/9r/9v8jzj2Qef8OTJs/j5QWMaUCGj267I0zFeQJalXNVXJFIjnKZMJzy6/wHDOkWQU+QZSVIh0oTTq895+epzVqtLukZwML/Pk4dj1psVeQ5pLlhvVtjBkCSCP/v5h5TZGC0zhsGS5hleeU7PX/PN63/ixctfMT+YoKSmbz2v377h+PghRS7pzQrjtyhhMV2P22EIbtFRGHixuOLBw4d89sPPmB1/gJ7cQ+QjMh/Q3ZaH40PuPXzGF7/9R3719VfMaGCi+eDZIzamxw+KPBdUhyOkUgzGo0lIhMMbyJTG+cjuOrp/SEXBelnTDoJif0wx2WNp+p3FvKdIK4QIbDYD/dCQZZqDg0lU6xtACMbTnMmsIElHHBxGoeVgBuo6ZTzVvHz5gnqziZnVMpIWklSTJiXz/UM++8GPePTkAybTyKQSQkW79+uxt7wm7YRv1U8PcB0h/N6m+V86vteiEC/hmjnEt2ilcGcBeG/u/61z3Sne12OoP/T7u7e7y4C6Pu7iA+/f54bx9d797jKlbi/3+gW+fa7vLkryDoMqdhjGeYKNhlhpkiBDQCc6CtR8ZBDpVKO8oNlGG+m2G7D9wNCsCKZBCYMQPqqZr03/dkMGnWpUpigzyaRMqTKJ7B2DM2giy8sHQ9+0INNIebQR+FNaIIl8/8H0aCVJdYIK8epTIel6h3B2BxR6los1TmQYapJixGQ2YTLL6NhSt2ucBuFTglAMfcvzr18yKsc8fPCAySjjwdE+b74Y0FrTbJZcnC4Y5RmZFIyrghAMTbNmPJ2DBJkKVBqZFIWecHD4I15+/jWbTcssGSFUw7Zt2G636GJKI0rQI7JygkTjQ4HUhsXlK1zrcRZCppA6RQdFZgN1V7OqG64WS3wQ7M328CbmC7gQoqeTFggHnbfUbc8qeDZSYNzAph7wecGDxw8Ji1Mumw2JTrn/4AFCJnStoZimBBlw9PR2yWgimYw+YDqdoBKNGTrenp0gBVhXoxNJohx9v2Kx6KiKCVqkbDY1b968Zr3Z8Ob0NZvW8PEPfhbxqRDfx0RLqjKjazvWywVv3rzg4PCQfmhZr1c7JawlS3KchSQr6bYOmUq8DVR6gpIJgpSUivVlzaSYo9OA0hZky7o549Xbzzm9fE6SxOSNR48f8cNP/wTvBWki2dQLfvObX7FabFitWt58c8YPn/w5IqR40+J8S5K3XNUveXX2JZt2yfbNEjNYtCywg6But+QJ6NQzGqds+47Fuma7HEhMwK8Me0nK/ME+2cEBfZKxHAxJ11LqnCxJyIsSLzy2b/nwox8hSbj4+p9Y9YZl1+OkIkk0R6MKKxTLyzWLtqfDoYsUnend9ENRZgVVVTEsOjb1htoqXNWTVg4pVNTRKIUSHp1qnE/QOmc6mzCbHdBsBy6vNhjjd3RQgXMdzhuMMzErWkNV5dy/f0w4vsdms0H4uCiU5Yg8nZDmBZPphDRNSdOEJInxu7farWvbnliL7uK3d2vW+5OWP+b4XuyjQEBcm7/vCmkEZd+d7wdxp8y+V7Dvnu/6n9tRW+92EX/ofohbSuv7x/tdxa0txi69yPubQn8zYtqd81ps5sOOCioFcbrI7eIX4j1CiH4pUazHzigui+wNaxms2zGPDDpRZFlO8I5+2+CNZdjWmG6LaRZ4syFRDl1oQm/x1iGFwAnonMWhmJYZ83HB4axE2pZ26PF9NJYTKmWw8TpE8EjlkTJgnEEhKNMU53qEsOAiaJ3nUf0apIxMJxlB8955sryimB6g85L94/tUswm92dDRQgIgaeslvQk0jaXMJbNJwaTMsX1LpgS5EAgz4L2kTBXdZkWVpaRJjhKWs5NXHB0/RISEJC+oRntUxZwkpBwdb3n28TkfHllc/TsSv6BbnUXaYOeZPPyYZ5/+jLKc8vL5Cw6PDffujfmb/9d/ZNu2WALNpqOsErJqBMaRbAfqzYbf/OY3KB3BPOcNeRFzb5XyKK2Y7M/YrlZ0psclmkEE1n6gU4okFbw8fUXnLRJBmuUcHNyj6x3b2gGbHWXZcXL+Bf/4q/+FUTnmFz/5M9q+Z315TqIDfb8Foksv3lGVgvGoQsqU5WWNHQyHhwekZfQSevzkIeAxpo8GKCJE/cnQs1ot6IeGP/mTnzGdzpjP9imyKDTre0tTd+zv3WPTQ5nN6cwV/dBRVRXBa5SsGI/vMyr2qOsF+weaJIM3p1/x8uR3WK6YHUpCMEglOF++RH+VMZscMpvMGFdTnj58xok8Rdgrfv1Pv+JHn/4V+wf76ArW2zPeLn5NPbzi/uMxWdWxXm1ABK4uL9jtMEh0HKfVnSYvcvJxRTd4bDOQTDTzwznZrOJ82FDXF+ihR22WzKb7HEz3mGYpaRrwqcaElA8//pRcdFx888988/acIC3WBvy2p/eGpjFsmx4rJeW8IFMVg+1QXlNmI/p2YLFcgFSMxhWOSAHNVEI7DGgtwXdY25EVgr29OfO9fRJdQmho24GNawEXE+AQDGbA2GFnlzPQ9luKMidNUubzGcf3jinLEikTMj3CusC2aXF2N352HqU1UsUNHTc1Ur6zANyl73+fheDu8T2B5lsOatzdXwO58r25vuCuRfaurr5zm7uF/7vsMf4QeHz92O9rHN7HIG6vmW+9aN8aPYXrRQ/EjmYZ/LXvkbrpJiIwDuLOuEspjVYKnaSAxPlA2zU3xoGjcQzjJkCelZi+p5MbrO0w/QZrtpFaqjMGZyOQ5zxOKvbu3SMtUsbjgnGVcG9e0a4GOutww6670BLp9S4G0e48VgSpdmgtULQkGqTSnL6NxnsPf3QPQsKqjqEe1kmkypkf7vHoox+wf/8RA+ClZLFeYFWHUwPttmXbN9RtR15NmY0OOJzdYzqaMbQDq6bl5Pk3JGG3GFnP/rhCeYPrNgQ3MB0XtM0a0zWU04okzSnG+ygxwQ2CJ89+zP5Yk9uv+PrXz7EdZKMp8/EedZ9Sze5RTo5AapJ8zOXbF5hmxeHRQ55+/AG/+8e/5c3JCpV7MqXAw2Ad3WBJi9hVdX2HlJY8VyRpLHpohc80JlH0CNpEssWythZdJhT7Jc3QI3WCNJ7JZMpsb592I1C6wHvFZrum6ddsuhNIGxabDTL5M4w1KBnYbK6YTAsCLVI4pIb5foES0VE3TRVJqhjNRhw9PqIbAlmZoZNIgx6GgYCLsY31muXqCi0VhwdztEqjCFIERmVBniWsXp/z6psT9u+VjIsj0iTHdHC8/5DTtwsm42M++/jPKcsRL19/xfniS1p7xfnVNzjVoooWqYdY/JSlq095ddVxvhwxG+1R5iVd3WKdZTZNeNvUbJqGsZ/QbN9ycvlrXp7/EpcsSSSM9xKsTyjKHOcDfWupmyUCi9Key2Wk2yZFzvx4jzQoCiQ6QK8tIRGs3QVuc8lsegDBs10vMD6weHvK0HRkSc694wc8evoB52+/4qpumE1LHj444PT0HN8a5CCQvcQj8Y0m6AQtPc4MBAN1G/M7kBorBEHGWjCqxoTQghhwbDGuiQaCSYaS4H200S+KnLqpMbbBmAap0iiALdNYP2TJZDLBWUeaZtEeoyyY7k1JdMbQROubjXdstzVpniOVuslzuY0G/u7a+F0b8O9ymv5Dxx+9KNz4E8m7lxPVru9TRK9znO/O60PYhU+/d/E3YPDd+9+577eK/W5d+kNjq7vdRvyad8553QVcG+TFDuF2hKT1rZ7Be7c7x13hh9gtdvGDIna6BGMsfd8zdC3r9Zq2a3Z0VEgTDT4QHNjB0dVbtuslXbPG2o4gRaRQygSZSIzxDD7w+P4xh/fmURHJwNtXL9De0TYt202HljFIAyFBuEjNlFEjYJ0FH5BItJAoKbh3UNB3O/puksVEq6ajNZL79+9z8OApSblH6xRBa7Z9FIkNxlOv12y7LTY41tstXQMMOd40fH75BYlM2G42NFdv2R8B2pOplL/6y79kuVrxu9//nun+Pnkq2VxtODt7w4fTA5RK0brCu4Qky0ik5MXJG15//p/RwxuOJ4KqGJNmUx7MDzmp13z9u3+ibQaGrmd/UvHNl//MvVlOlo7Is5yHDySD9Rhv6UzMsfYCBjPEl0pAnkJeZmSlxg09XjhOF5dsncUmCpdJVJKRiRSrRQRoAeEE42rCfHZA17lor6zHZFnBYFbkume9OSPNHEM74NzAbDbj189/x6sXn/Pso0fcz/bwIgaxF/mIPMtotzC0Dq1htjfGqUDTb/HBMh5XNL0FPGboI4aVaA4PD3HWAZ4s1UgBeIszJiqN9w44PRvjBk+zskz29tnILcFU/PDTT5jvPWQyOsS4jsH2vDn/mnX3EpG0ZJkk+A6V2ih+6xzlROBNh+sHlt0lhhHeCYTKMAbuPzqgGpd0puFi8Zp/+NV/Ja0aeneFRZOPRsh1oOla8jKJMZRZSr1ZRqaNTuj7hrrdQpBoFOlkRkDQix4le6RKSLWm3b5iEZaUewfUyw3b5TntumUwgZM3z/nBZ59xcPyY09fPCaRsa0uWjhlMR2h7unagR1JMs93nL2aAmM5hmoa98ZS+sTTWgrZszy+ZhgQlUwIDFkOiY0TudmNJdcSYRlWOcwGlBNb1dF1NmsXwK4ibyKIomU33uC6HTbMbjyYSLVO8UTthY8JyvWE4NRwGz2g8iSMjYuZKxAhvRcHf1Sl814b7Xzq+V8iOC7d6gOtxzN2CfE3x3EHI3znjf9/gDtiF2bw7Mrr+/bfwA8LN9Oh9APv6id99ISIj6r2W5W6XwbviuNvnB9c224TbxDi3i7oLwYP0SMUOFA60TUtdb+jbqHb23tFsapTwJDrFDYHF5QWXZycMzSV+2BKswyUJTqUY4WMqFBGMfn16wv7RlEyD9IF+27JpO84Xa7rNQKEFoEkSiXeOoEIUvoiY6xxHj1F4N6pGiDLB9AHrFDItePDsPslVg6VgeviUpJxjVErX78QyWhGsZ+hqTt6sme5NGRUJ3uTM9+/hjKJpelwbH9+2giyZ4MyWxg3kqeDy/IrzsxNs1+LNgBaBZrPhm6++4vHTzyikoO1atFYIJGmR86/+8i94+/V/5vJsi/agJ4Bdcr/a45On9/n157/H+cDh3pxXz/8Z3y05edmxPP0GBYzygm3fU3c9zeDwKlBOMhDRsjgIGE1LptOSNIEuKESidiItTzaZoVRgNqvomiU6kWS5hsZSZAV5VnK4d0TfWDIN5agk+Ij/XF2d0w8NZZmzP73HyclbCj1DacXPf/ELAh3NdkNVJXRDiwjRqiDLCsbTlMEOoFw0PixStts1g3nJatMzGk/o+g6l1Q5fcJhh4MH9I9xOQJmmCoQHAuPRmH/9r/+KL776NVfLS1TIePzwh8wP5iR6RJEf0faCdhjwCi4351ixQtDSO4fzjmKkCVKgMw106NxgM09SKCSQFiO0TzGN5oNHn7K3P8P4hiSFopQkBTRDz+BavAPnDW3bRlzKRTVxkaVo5QnBEHaZ50GAM5ZNvSIZjVEiKt0zITHDgHKSWTbheFJyvlzgEsloOmK1ablarPjbv/lbfvqTnzGZdSyX51RZ3K3rLGN9ckIxOSRNcnQ548//7b+jEB3/5X/+j3gnCVbSdA6DxskE5xVda3BXa4qsIM0NUgZ0lmCkx5oBpT3jcY4QGV3fc7A/242e/G5zKdFKkSYZ4/EUtXNTtmYgUbDqtjSbKGhU5GRZSVlmeDy9sVHrUhTILCWKRgNqh3G696j17y8O3/f4fkDzDhC+BZN31qzvj/25/eHdgnt3IYC79hXfLu7Xv/8WRhCI3cofGDe9vzjI7zgvcCP0ECJyE9+9H99aKO7qK+6ez1qz263F30mpdvnMgmDB2p62DTjds7pas7x4y3Z9jjAbVBgQAQYTzcZ6u+smrKftB/x6HdvHSY71niHA24sVy/WAcJrBGKw3VIXCawfSk2iF0GADEXhOUoSC0XiPvekBr16eko72yOeH/PCnf8qvP/8akhHZ6JCkmNI6gdcpKkuRiWYPz3oxYn/viHJUkGYaqTSgcYOkbx3SK4KLGdPOrFkvXvD1F7/j/GLDf/n//jVFpsiLlG7botISJQRX5xecvn5LNp7jhCbLPJ1RmCagXU0/JCxXjnEiWfgVW7Gk3q6Yr8+4tzfjN7/5Ld3VG1QIhL7GG8NgFT5YXOvxUqLTEpzBixhCpBONw6NUoKhyggzY4MmqjGo0wraOy4sryixH48iqMZltScoU7yEozagYc7j/iHGxRzAVWmTYIQoBE6U5P7lgs9hS5CB1z+nF12wXlp/+6GOKDJqtww7tziXToPVAvV2htaFuDE2/pSRHpxnjSY4xHW/fXvLy1Ql12zGZzvjpz3+OAMazKWVeMqoK6tWS9bKmHxxVFXE6LzxuGNA6Q5ByeVkzmY7AlQxO0WwNQitOzy/4/fMvIwCcJxCGSJUWgu3KIhtIMk+SCvIiISs1wilSkSFMgTQVuZ5w/+EHOwtoj1SaohjRmRoldbQ411DlI9RszKg8RJFycXJGF8C7But6tIYs0VgvUDpnWk4pVILyDtqWTCbMyikJCaKTvP7qDauzK9xgKbIRs+kxy9Ub1puWL5+/4enTh5xdLsEpEpVhg2Z69IT9R084ePwUnybMjo4RmzO0SAjW47yiNwqyislkH5dWPDm8z2azpV7XKOd4+/ot1rd88PQB0+mYLNOkWbTGHk1ydCaZTKc45xkGSVHuoaXG+0C6C+Ty3iCFZxga+n5NXgRCSEizlCQVFFVKVmR0vQGhd8Jbj0CipIxyAN4dD93Q7N8br39XDfxDx/dfFHi3gN/92Xc96Lu7/3e7CtitZO/N+N9/Uu9exLtzs/e7hW/5IL0ninv3udxiCTeLy84G46ZLiDfiRpS3c1oFFb93JgbS7y5OiOhhIgIECc4MbFcNGzewPDunq6/IQkeifNyFW8fgHc5LnLHxQ2kGrLMcjg8oixxjLc4F6t6z7ByrQaG8INt1Y8b2oCOdNUksUscRWVFlBFEitKRzik3vcTJh795DRkePOb1qmB8/ZjtAMZrgZUqusqjWJZoeBmA2uYcdxvgwREUwMRxIhixqLhx458nSgqAF09mPKIoZL7/6PadvXmJNx2qzRsiE0Sxncb6g7gRnb094/PHHeLvF6wTvFJvGEJo1RXFA22jqjSd1A+QWs462BIvsjO3qgk5oEqEZmjjSsCYGwJNpvBLIqkKkJTqP4jyGDq0laSKQSUJvOjpvIBhWTYdROUolOCcoijHbdUcSEpSLiuZcj8EoDqb3qdIZJhQIJ5FAnmiazQLpE37x47/gd5//I84J/uJP/xWmF2w2DSGk1FuDt5bz8yt+9U+/5E/+9Mc8+eAD2mHJ+WKJ8xKVHVMUCiE8bbum7RbMDjR+4fny+T+iS8GTxx8TVMJ8NCZ4R55XDGWMarQuZvkKIehtXBQOD+9zfnGCCBluUNHSxAvyJOHRw4dcXM3YbnPC0KJ0jkIivMf4OJIUxmLdQGsGpIo05AfHH9FtMuy2YlI8RIgR3kc216Q64MG9T3n1pqcLHa4b2LoB4SoeP/iYxw9+SCZLug+2nL39irevf8fp2dckIhBcQAfNwf4hTx88Y3F2yfnrtyQOlM4YlXsokVAvN5wvVuig8YPAesEoLdDFnEpbvn57zv6jx/z0L/+Kl69esl5vefrhZ/zFxz9AlyOyyZh0lLNcnPPb3/42hudkBUIWpOUe4/1jTDZGVhPmB/eoJj32oGW5eMNm03F5dUmaSJ48foAUAe+73SRjQAiPsza6zhYVeTbCWkfX9bc+WRhCMBi7ZjBL+t6Q5RVJOiEvJFI6nLE776NINhASgspA76ipUt2aeX5HXXx/cvLHHN+Tkipu8IKbCxDfsaPn2tX0XXDX+28zioS4nX3dfTI353pvcbj727tP/LtGWreMoT98vKPUDgIR5M33dy/l2roiStoVAY8UMRDH2YG+u82TCM7FpCxvsENPvVnQ1ktsc4UYtuR0pMJj/bAL4xY4H3a+6j66ls4nPDg+io8bXASwnWBtFNuQowMYC5JApj0iOHCBwUMaEvJyRDU+ZHY4oyjTXaYDPPr4HvN7z7BqhPfQG8d4NmVVN3hhcLQElSKkJoSouO6Nw3tDkkoUGh88KkBwnt50pCoh0xJj+wjQ6ZL942cImbFabnn9zeeUhWazbqhGEGzAbA1vX77FNg6UYdNdAAoZwA81D+4/5qvigNXiDaUQZFLgraFva3zY4n1gNB3hekffd2hR4qWld46htQx4Sh3Q+QipXcyJFhYhRbTuUDnNtolMDi/oB8OyH3BJwtAv6YsekwtEpXDGUegC5QsKOeVo+gQ3pEiXMK4mBC8wfcebF2+YT+/x8x9/ygf3n/H21SvauuXs5IrDo3usVmu0dGipGHp48PAxk8k8MltSsKFhVdc8Eg8wJuHv/v7v0UnGy9dvGZxjNj8gqwaW6zeMNhNy6whSM0rzOF4cYvyiTjR938eITSWw1nFwcMh6s6ZtO2RI2Cw24MEMCZeL15w8P8EOsegiIxOvLFKMHwi9R4sUrwTOOoLM2LaCbZGQiX2m42Pm08dU2R7OKrwN4Ebsjz6iGbUEozE6YblZ4wdBerhHoe8x1IFCjfjsgxlPDx/w1/9Lx7a5jEaReclYjPArz9U3K9bnDUd7Y8w2UEuLTjRCT5kcTBHBkeqULC9JizHzDz+j7nrSb75mYS3z8R6f/Nkj1uuGJ88+oaxmJHmBE543p6/451/+ryy+fo4SEiE0RVmyt/+Qcv8BawskBa3xCCGxznF5uaDMZ/R5gzfQbpvog2YisDwM8W/z9LRmNJlxsJfj3YCzcQMiRZx4KGExboMPG0JY0XU1VXUfIQasbbDeYG2g3jasNy1ZPqIsKtIkoyoqJpMZidLvgMjX/9/FFN7HZ/+l43uxjyJ5/8686s4u+66yOOyon++Ayd8BHN96fP/vs4zuzsfeX/D+e2lXN+fnbvGPJn3BXzvASpS+S521WBezmkOQJEpEqwLnsHaIky0pb0J0hLc4O2CGHts35Gog0KF9S+IdKgS8jKaoDhBKYb1DJ4qjgznT6YShb9GpZDCO3gtaJ7EyQaoEFyQyhSIHRMfgos2G8il5OWe2/yhaI09ydBIXzHI0wQaFkBlm6AlBslyuWG230aRPJOgkR8ps570iopOkiBbH1vXxDzJAkioIFueGmG28Oictx0yLe1GFPZrz8Wc/Ynl1TlcvwAvsYLG9ZegHTGNYX/RUjHCyZ/BbMi2QfkBIw2xvj0V9hukFi65F4mm7Ovo0SRCixxmP8YpNMyC1prEDXRjwiaC+qhnPJuh0hBwcxjRoBNPpIXkmaeqGmExhUImiRLAxAlTKsHV0faBZbCjGCVlVIYXlo8ePSdWEuvYIBA/vHeND4PLiEhFgUs7BpMxGR8h7itcvX/Lw/j3mRw9I05REBfpuST2qMMM+3js2dU01LXj0+JjRZkVdX7Jdb1CqJ8sUDx6MOV9c4Vlh3Yq6zTBuS7eC5bJhnI04mO6RZzlaxSCWmNYXWG9WNyr+pm2pihFd3ZHrlCrLyPOE8vgY6X7C55//PRfnSwQDWRromg6VGUQ6IJVGeAFBIXXBMCS4JqHzUIwSqmyCDArjHEVRUeqKMotuo03dUmWes6bmaH6fUs/JxJyizEiEoF2/4sUXbynUCC+ayKiaHpOnY6RNyULFo4OKTz99iLEDVqSotCTJiphN7izj0QiVJPQuIHTCTM746fEh5agiBDg7XzA5OGbVB7auJesdaSoRQTCqSpokwQmJMY5qUkRDSJlRVBW9FEiVsVyecfLyG4becHT4kOmkoqoCaerwO1+rRCe4JNAPNlqFd4ZEZoxHfmdpYxDo2FmEAWNqhn7B0C8IQWDtaPfzFpUGiqKkGxLcckXT1FjryHSOEorR6HYK8l2+cf+9x/daFKJG4b2LuNX47jib8Usloh2F2hnkhZ0Fxk4etouhkzfTnWsc4noYJUQMp/Fhl/YW4YwbK4o/NCf79kztNh/h/RUl3ub2Od2Mwt5jT8UFLO7iI2gUgKgmZZcIVuQxm8BaEx1EdxJ0sGTCoBOP6JtIRwxDjMdzHulBekES1I5FZFEy0Lc1fbNFZxrjBJ1XeKkJMokBPdKQ5Zq8Ukg5MD84YFNvWNcdXqVkoz1Gs2OyIgJwZqjxQ8Nmsya1isP7M4bec3l5TmejN5IXPUmW794nC2gSne+8WAxKWFy7ZnO2xlnLeLLH29NzTs8u+MEPf0C/WfLFl7/nhz/71wgkrh3IihkHh494Uddse0ey2TKd7fHhx48px4eslxeUexWTKqFuOrAtwnbU67dMJoq6UASt2NSwv39IkWecXVywbVsuNgvM4BmPKupNh0oSLAGvE4zzGN8TdEdepMgsR0nPtqv57RdfURUaZ3rKNEUEj/OCHk1ejHjw4DHzBw/57ddfcLI4o1l11OuO+eExD/c/xLaScTmm3Q6YwVKNRnzw9CmzSUVZapypaeqBpu3J04y9+R4yK3ZqekmWpFTFGDNsOb98ifXw+y++4fnLL9CZ5M//7C+pRhn3uUdd11Q6I6gxg7VoPSBlx7a+QkrBdLLHfLpHlVe7qNmd5w0xqGV+eIjH07RbfIi24iEIDg/voZOAwJFnJR8/+wzbtvTrmrO3z1l1K0Yjyf2HJXiLGKJAU6mSrEyZ5EfIoUKpCnzKcrlhUimwgiofURQliH3G5ZgqH/Pm9ZfQT/m3f/nvkX6E70uES8ikYBAZL74+iQH2ZNw7OGZW7SMcZGnCh/eOmI0KEj1Q246q0Ozfm6Lzkm3b05lAOk4RSYaQGhPieEwZz/zwmKIYEfRbzk4vmaoUnWourq7I04RvXnzN5ekFQ2/Bx1rW9wNn52dMVUm2l+GCpOs61ssVpu84PjqmqAqMmbBcvOJ63GytoSwzrHVIaRmNcrRMccbQd1uGoWZbr8h26YlpKkj0gHdr8BskKcG2CHqSpNixNT2TyZgQNE1roqu+3Nnr7JIor11Vr9PHQvA4EzM4hFJE9qTkj90/fz+bi3C9COwO73dOf9dz/Hgjwc6K1O7m81LGPAAfoke4EAR3PcOPbZnYLQg7kcCtmhjQu6J83VeEEGfYcNsmxfmauKWs7ub77tqD6b2xVfRnukXsrzsdHyDRCudEBJG9QKh47usQHecCzkFwluDiohDpqRIlE7z08fbBQdgi+0tMc4F0GyTRbsKHQBAeJSCTAWEHgo8KSBsCm/WC07eSp598hs8KhqHBBofpDUnoKQSUmshoyAWTWUbvajKv6U1gsn9IOT0gTQTWeUzvCdZih5qhWaGoGYxidXKBSkfkoz1komk3Gzq/Js1ytk2HEBHQxnRcvX1Bd3VKLjxaKqY//Anu7Sl+sebNb3sePn7MqW3pN2cIkaGFxnsJuqC1grq37Gc5zz4+JMumVKMJQV8wDAOuKwmbBb6vGfot28VrvL/C6TWnjaMcF9z79CecvV0jOlisXjPOSlQluLAOPZ4wms3JspyXr16z2daIRNB6S5GPwEMxmqKHhHp1xcVqS5EktE1HJhS9ccj9CQ+OHwOKxdsTKivZF2MKOYZkxMePf8E0OaAzajeq0ZyfXyKExnnD4uqS86uWRA5slmc403J8eEiSSNAGITRaJQSf4qwiqJRiHBXOX3z991wuG4S2fP3qFb/42T5P5k8RQbNcrKjrFXWz4ur8EusHuvUVzz78iPv3HqODigExSkWPrRABT+t7lEogRI+koihYbTaMxvvoomC1viBg2G5WtNs19dry+P6n7JVTXn71W7rNJW4pCYMj8QPSWoR0bIJj/uw+xXhCY3Iuh5a6PqOuWj5+/BllkqOCwDpJme5zf//H5OKIDx/+JSJE63UpPIGWxXLB11/9jh//+Od88/xzVosriuIxUmWkwiCaJbI7oWt7Fv2CeluDknRv7zGeH3CxqjEiJ9s7Ynr8lNnxY3RSYjpDkcCw6XENTPMJet/jMXz15d/xu9/9locPnjDbO0TKjKtFTxUEXnqM6wm9Y716y15VsO0tWVlytDdlXmqsMSASpC7Zdg6HY9TGpDuCI80CqjEEP+CDo2s13rZsmjfU9RmzyYw02UeJglEhmY0cvotTCul6cBuC06hkhpISQUJRgEdirEMEgQ2GwWwJ0iHCrgZ7GHqDt4ahv87liIprqfI42v0fuSgEISJVjIh+X++6rxuYW02AjAYQ18EQ1zN+BGZnB6GSW29vKeXOUZU7O/ldx6DkDX00RK9P8FFRfHNd36F8vqVlXY+gIkUvhLsijqjEjovDnXFWuF6Fr1PeruXkkjRN6PuOvu/iAuGi+jhYh3UdiU4ZXPQzEqFH+Q7XbcG1JNKRJzlKgB8spul3HdN1FKckCImXErWjmXVdz+n5FePD+6ikwLXbSGVTjkRFIwzvHM4pXr54jfGBNK+iT5GWjKocISxNvWR9dYrwW/xQ0w89b09e0w6wXA1UkyMeZBngubo4J81zEqVYnJ8hVYJOEzQO3IDvG5Ii7vBe/P43GCSlFnTLS/zBjHmV4bqaLBN4b3CmZ7W4YH8+4fh4j7wQSDmQJgYpOrwf2Cxr+mVgu7pEmAY/1DhTo5VjNs25WjRMZ4dsWgtpRT6Z86wao5P4PqV5xng8ilGUdYtMUzJfobKCvNxjPD2iH1qCtyALZsmY9dUlzXoDVsROSUgeTA/JshF26FlcXvHmzVscGhMSVKF5fP8ZfefxMokiNqHo2o43r79BSEegZVOfIUVP16wY5TlCgXOG3rTkRRrFT0GQlxlKa4rxjKTY5/7bD0hGGUI7prN7LK56jvZmCJ/gywxNQaYrfvqDkt998YIyKZiVE6QP5Fm0NwFFWZbRc18nuOBweFxvCEEwHo1ZLTacnr5mMikQysckNaWpqinCC5oVzGYP2PvFPr/6p7/h7OyUUQoCiQ6Svm+RZcnR/JC26TEioTeGtbGw58k/ync71jgpaHtDux3I9Iiz0xPevH7Nj3/8Q7CObb+lbrf84Mc/YT6t+Pjjj/j1r39NUZbIYGk3l9BFF1LlLQXROQAE25Mz+qs1TuU4ZWhtwsVZww/liKefPGTla4QgJgJ6EzdpOmD6lpOT53T9ktOziJv88LMPkesFL37za2bjksVqFbO5SRgf9eRZEW1hdEKRTGi7hs45sixnPj8khJokjR5Z1kVSSIyv9QymxRjL5WJJ3bxBa4P1AmM11lra1iJkw3gqUEKitcPaDYUckWiPkDHHvSxTVBpf/+AcfXdBrTrGch9vEpz12MHRNjVNUzMMLVHcqsiyEePxHlU1/h+7KHC9Cb/ZjEfZ193f7UpyHJvcHf3sirTetbfv6xrE9YiJ6xHOHZzhzkJx97irYXgft7gVrxGpW9ydubGjg4XdNbwvqJP4XWjLdWQnxA5HqQIzDHRdhxmGuODtWjVjLEI0MYzbGTLlCH6L61tUcOSpZpynEUjVlt4nbG1D5wbwEuHBS0XYqaVDsDSdoX5zwoN0jKomBKFQOo0OqsLjPRjrYmKUzMjThAf3HzN4z5tXz9mfl4xHCacn37C4eEGmHIoBYz1141iue9o+8Lis6Lsl22XPdrPl4tyQJAWzvXuMRlOkUnjT0Wc5rRAc3Tvih599xtViyavXJ8yPjhmM5fU3X/Lokx+w9ZbXX/8+BppoxdFexnJpcf2Ck1ex3Z5NDwgiIdkptkdlimuXBLMG3yJ8j7eeIlXMxxMWJys2S8H44Jh7j57ig+Xy8ows1ygRtQKbzZrRrGS2KSmrI9JiTtcLnFVAgU4lqYyUx+nkAeurSzbLKy7PT5mMJ0zH+6yvamazKcurF2zrASc8Qwh8+uwhQiYgFYMxFLrADp4sl/z//vo/s20vODoqkHpgMk7omprp42csr85pm4YkHzCDxLoOCFSjScy92Dvmi6+fs390iCwUl6szmi7w4vdfMs4eUaUTqmIfkVX86h+/RCeKv/rzf8+6DsyqGVJpzk9Oubxa8pOf/IwkSXDO0/c9WmuKLCFJYge6f/BzcJ6TtydkieSDD5/RDwPb7ZbVYolSKaNqzOrynDLT/Mm/mnB5+QVvX/2KxeqcXCqCFORpwsn5KUcfHFNvtihRYAfDkwePQKpYF0TMk95sNjTbhizRTMYT1OPIZjLB0g0D27Znbzphudmis5Kjh4/xLuBMjzcGawYad0XXDSSNQTmPVJKhcchKkpQpUmZgFEEm/K//+a/53T99zfGzD5gdHZGXZRSGYhlCj8wD1TRlZnPyRPDV739JIT0//dmPmGQpv/nnXyIQKKHJdgzEsoiGkYjo7jyZZLj1BiEc09mMprY0TUee610us8WYIY5/hKcftrTDBTp1jEYlQoobH6SmrQkYsiIghSFQo/QYY1YIlZAnOUFEbC/VQDAIDN6uWK/PGPoLvM9pGkPXRqPNWB9BKlBWEYn5gma7/aNK/X+XS2rcZb+vN7gt5H43MpLvDbG0VjcF+v1O4f3jOoPgtgO5S1mV31oI3j/+EIvpNm3t23oJYIdjxHi82+YldgVKKaqqwDlD0zRYM9C1HXaIgen90OOdpyxShAoE5/C23ymSo/Hc0Blsb/AmIIOIOI0HgojUzhDwQoLQO2aWQqgEgsK7OA92DuzurR5CIHhPoTXBOc5Pzuj9wGp9gZI1946mbOtLTLcCHSKDZ/BsG0/wkixN2K4veWlb6rajblq6zjEZH/D04VNEiCE0RZbTpBlpklBWFQHP0f0jnn38EUM3cH5ySrdMuHrzDfMnnzIfJXz+6gtGZUZZCsa5pcghTzx5XjCbZVjjafstdANZOWE6zTB9SrOtccbgQ0CLlHbTUmYHzI8ecfjsQzpj2G7XzPYPIPQoGbhantNuNzx+9BB3f0ZeFBwc3seFjLY3NE1PNxi6YQCVIRGM5/sIpWiHnmcff0SRjzBD4Mvff8PJ2ysQis4FZkf7/Phnf4oPMmoSkgJ8zF/om4bV1Tl1e8p225IVFil6tnW9Mwh8Sl5IyjKQ5Q2Dq1EplFUMJeqHgourMzatYbldYLGYAB9/+hMSPca4hO3W453i0YMf4IMl0zOmlSTXOYE4W267ji+/+pKHDx9TlhXGOpJM44xBiVgQZsmYybgkuDmzcRn1ANbGiM/gyfKc8f4+e/uHXJydo0k4nmZsTMMqSNbLMySC1XLFr1//NX+ZHzCbP8H0HqkTum1L1w14NFmAvutp2zZuvIRmNKp28aID3jo2mw2rTY01PUqEGIaUJoBAJTmWBJFUpOMjXp1ckrWOKtEIKWg6QShSMp/hncb1gWRc0G7XvHjzGxov+cXBA1RS0JmOy+UFKh24vHzN+eVbqkwTTIMfVlyefsP9T4949tEnnJye8ObVK0Z5RV6OkFoTvCcvC6QQdG1DkmWUVUnfb0iTgk4lLBbnpKm6CeIKIeBDVKGnuWaWzcjSGeBpa4PtDFo6jBlIUolOBUiHoMeHmkBK1ymc06T5HKUyvHco0RNCS5I2rNdnbNavSPUIqSqyNCNNMhAJCEmSRL+xPK+QKNpu+M5a+f7x37kowA5geE9fcPv/+yOda6TY7ewlrkdIMSGLqBq+KfLvy7N3j7d73OsQ6mtg+Jr++q567/p8O+/876Btfdtd9e4Cd1fIdo2h+52TYUEInl56nFN4y26UEfObtQQto1NlCAbpDT702N5hugHTGzDgTSy4cvc8ojoRggYXwBmPThMSnSGTFCk1znnsYBHBgnIE77HWEaTBu57F8pK9g5x7RyMS0XN28jVKWGQwtEPAdDGuMngoiow0L+hdx3azRUiFFgNFqpBhQAbDZDzDukCWaNifc/VG8OrVC5aLM/71X/5rNotL/vq//ldwjmAtq8FT94ZqMmZWOKrCcng4Rog5xmzpjcS6Bt+foaSOORIhQQdNIhJkkiCLEZ3Q4A0qH/Hsw8ccHjzFiIzOWYwdCMFGIzWpWF6dcnlxgsCwKhwSTyYDMlxQlhPSTHAwL7laNfz+i1eYIUdLSdc1JInkwZNjgoS+7bg8X/D29RnBK5yIoT2f/fCnTGYHOJ1hPGgVffMTKfjlP/8jp2/e0A2XzOaSyajCh5aqUJyePOfJwyMm45w88STZgBcemYBQBjcYrLMIqdk0S6ww1P2KFycDT/7kY5wmbiCkosxL9g4e4Kwhy8eMpxXOaawVfPThRxzeO+bs7CL+PbiAMTH06dp7K1gTNzZCcnR4gBCwXq1oB0PAk5cZUghkpsmKhGnY55tvasp8Sj5/SGctuiqxbU0SJNps+Lu//Rs+/tgwLg8ZFVNOX71l0wYOj+9TlgWLxSUQGFdl9HbSGq0l3giWVwtevXhBXuQIBMMwkKc6BtcTmX5t0yJsIB8fMLv3AZvLE07rVcT1KECNqNIJjYk6H9qBycPH/OjfPuPDz36CSnJMMJFAkaecXb7hl//4d+xPMrq2ppQa6QZctyXPUoyQ3H/8Aa/enlGOZwgZ1dOj6ZzxaMS2adls2x3DMI7Co0dayjDAyekVs2nJZFrdxOimqUbpHOsyEiVIk4L96Zi9ySGzyYi6uUDIDXXzCuM3SOEx/RYhS7xtsaahqg4oqhJjeurthm2zxruGqgSrOwgNUowQYoKzOd0gKYs9sixFyajlqcoJ0/EfV+6/1/goltqw0yvczvrv7txv8IB3dAvcFFngxjLiFnN416/o+rjOMLguztcX8m53cOsWyJ3HvRlRBb9jPt3+/Fsqae7SX6/jN69B5R3GoMTNohEZHpJhACVF/B2CUTnCDAN9W9N1PfRN9NUxPbiW2nhkkLevnwjRSCuI2DX4yBgRKKTUWO/wCPrBkur4h+5dZC0FGTsz6xzWD3g/kCaC/YOS+w/2EHLA+xYferSMYzTjPFonkdedFaA1QgaqVNMOFo8hURacRYUB37ekeNIkxfYtptkirEWlih989CHrqwv+7m//Dm8NfhjwwwBOsDl7TRhGzErBwwdjhOwxpgbRUpQSoRSBSMUcZSV937JdDYyOHpCm4zjuEQkoy7b1HO5PWaxX9E4iygKPZ5wrzNDw5psvEaFjPpKUeYntLiirjCrT5GpBlVkSK3FhS6YsR/spWTHi4vyK87PX5GlKogQLe8HyosGawHQ85fRyEQOF0pynzz7BWEAJirzAek+Z5ajgKLOCn/zwpwRfE0RNkrVsmp6Tq7esl1s++eSAo6NnJKIlSXqCcDgRzdj61tH2DbO9e/z+1Sv0yJOmnnp1wfPT3/PR05TZ7JCubqOWYZTDkDDdP6TvPSpJqZICoTSTyZQsqzCDpWkahBDs7c9o+4aws5aYjEruHR5Q1zXbuqEfLEEIvHAsVxecnL3ho48/RCjFRb0gnWS8vjhldPCQtCxYn7+kXlyQCkFaw9Xllv/213/NbHSPX/z0z0kOKwiBptlS1yvOzk7o2obxeMTx0RHe9JR5jhkGvvj9FyRS4u3AaD6FkDF0LVJ4FLBYXCCCoyxKnj58zM//5M8ZTE/XN7RNwzfPn7OuawYhWK9rTBB8+MEnHD14xHg2x8iUJK1w3QqVJNBHmubebI9uu2ScFPSrmkJl+N7FLi/f49HTj/jy+SvyaozUKcHHDl/iUTJqPi4uryjLuMAF4sx+NJ5Tby5Zb/roZiqjXcdsNmM2PcQYSd8NSJFSlVP2ZoeUeUqaFqikI+C4WgwMbk2RaZQckDqQpilVXpDoDIVEVlPwDVeXC4xpyLIea7fU60tEmCDFHmbQNKYnVYG0nKJkh7Gg5P9goPma1hn1CrvAmYjTcs3uuV4mbv67aR1i0fXeo5MEoeRt6M37wgPieChSQG+7g9vffdsF8CZOc6c2vvt7KXc20d+h7Lv9Oi46d7OZrzUR4hpgDyHu0q3dGea5+C84CBFY11Ki8xxnekxX403EHcQumUyJsLv9NT0sEspsCHgkjoB1bsfQ0oBCydjiZ4KdGZpDCE+aaLTymM4hgqUcKfamOfN5SaItLvQo6UhVZFNJJGku0Tql7QeCUogdE8wTZ5DOWRJNdIENhvPTV+RJQQiKq/NTvv79rymkYTQ7RDjHq5cvGNotwXukc8zGI3Q/8GpxRd8t+PTTh2SyQ8geqS1D1zCZzvng2Qf01vDrX/+W6UTjfUbXwtBZlM4RYoKUOXkh8PQEqQkqtuVlrrDWcfLqS7arC6pcsD8fgW8pC4UQGUWZEdjSLq/oG4XUJSqdcLz/gEf3j3l7siQMFV93A23X0QRPqjK6riUEyeHhAaumo+4Gju4/ohxNkGmGlwniOvpSQbCejz/8iHpzgB22LNevqZvXtOuWR/f3+Xf//k+ZzTMG85o084CPHlWiJMvGCC+4vGg5enTMZDrDZQuGoScfwdurbzh+dExiFdloRF0vyar7TCZ79N6D1iiVMhiH8OLm86t1Qpo6tNY38Zmr1RXb1Zp+XGKs2QGhgmA8xjvaYcPnv/snXp98w7J5w4MHj3FO4L0myTIO7j3A9lO22y1sB4wZmO4d0XcLztZLFmbBxcUF9XbgXz19RpLu9C0q/lssLhi6Lft7c5ok48VXz/kv/+W/8PHHz7i4OOXf/Nt/w/HxEWvfk8gQbeXbNTIE7h8/4ckHH6CSDKM0ddsytoYPf/rnOOd49eYVm22LCYLZwRGddfRE8keOoBqNWaw35HnCuCoJNjDKx4S2Z9h6pAm4tuHqas3xw0Mme4cc3n/E0Pdkec7Qd6yXlyipMESKbNMapIwUVGsNOkmZz4+Zz+c0zRK3Y3uJ4MjzjCRNGFXTyEy0gnqz5eLyjGAtTbvCug1S1bhdvYnJfBKBpsgylJS7nGWBRJAlEcxv6xYlDUo5lHQE0yGoSaMJCKYJ9NSEvML5lDTN/qhS/71cUm+9NWA3cb8FhkVkKFwrhAPRwvkd7yAZFdHcUdoJcbug3IjdZNQKOOeihFvc0R6Ed7uB/z3Vntg91jWD6K7t7F1319txUSzwsUOJ4iSCAy+QWkVRnndY02OGSP0KzhHCjgV0TdGVEpC7ZLaAsAFcuFkMxa478iJiAzZ4bAAvBOjYRcg7FFgpoO9amnqDNR2Yjs4HLJbgeyYjxd5+waRSqMRRFCnj8QEnb1+SJQotFdY4dJKS5yWj6YzFZoUPgIp6Ch0SpBI7t0gIVrC8uqBebvnfWPvPHkvTNL8T+93usceEzUhTWbar3XSP3eFwuYvVvhKEhRYC9NX2jb6BAC30QoIESACXpCQuOeTssGe62dOmqquy0oc/7nG31Yv7RGRWzVDiAB1AIDPMMRFxnuu6r+vvVqsd43aLigOzZUt3c8Mv/uoG6z0KmKwDAe2sZXlyxM3wFUUNKnXY3jJflpRGYcSMShu2qy2js5Ra0q1u9ljJnM72mLpmdAnrFarQoCTd5JBKMg47bq/fsLm9oioEB5Xm0YM5R4sSrSo+//wjzi9e8ubtS4rSMLiRUrbE6IhjRyi21MWMTz44Axc5WbYcLY948+acvh9omwJtSl6+eYFLHhs8f/Jnf0ZRldgEpihyVKpSGG1oZjMO54ds2zm31+dsN+doofjw8Rl/9KePmLeOoT9HiDxBVWWNUjUIw9gnSnPIk8cLojJsd2tq4/Nqsk64LvDN668Yj0dKs2DqEp21/PjTP6LvJ2IIKAEi5UPPerXlYLmkLAuapmG73bK6vubf/uW/5vrqLd///mds5zVKG7xPSAVEwXa7w1QRhKfrb3j5uiOJicPDhyjRcHx8jFIFQVUgG86efI+p21GqggcnE9P3Ak15wNHBA5IskDKiVWKaRoIfKAqRswySZ71ZM/UT1gf+1/+b/45HZ8d88+xLQvSsVrfYcaCcFbhhS5q2mLLGjjt8cAhTE5OhLAzRRBIS6wfW6wllSj756CNsiIhppCjrvbjTQ7JUleT1Vy958/oZtdToKJhGR5wMbnJopXn1+oqTx58jhOLhkw/5za9/hVbjPgrWoZXENDOUEvvrcczRtsoghaSdzzAGDg4O6bprnN0SU16XS+H3QkRDU1YUxcR6c83V+oqL8wuGYcvBoUaXI8N4STycUfsSJVvqMme4S6UhBVK0FCZxdDgj+JKULCkICq2JITCsrxBRUc9Huv4l3VZSzRcUbUuZmt9vU7grwu/+vXt/r6gS76mg380siHf//w5jSHzHGzymBPH9VCH2fiJ3DYT7/98V9XRf+O+YS+zv811DyI95hyN8m4109zPcraDgLieC9x4jj4Mxhnu7izuq6933O+f2uczgfMKFRPICEWWm6YU9mBwSMgpSyH4tCUVeWmXmUYTMRoqJYC3TMODHCTt1GJ0BjuAmvHNUlWBx0NDOS+paIkWgrsscuCPISXBKYrTBumyah4goI4k+gsjUQWMEoHHWZUBDZf/+bd/hR48II6UEt+sIBITKXk0uRkRhGKzjV797ycNHSz756BQhLVp5+n5LWc0BgXURJQsmZQkpojBIAt55Qgo0bXWfW2092RnTZFsNkSTdrkOEkaNFiwgji6bgn/zxH/L6xRes12tsv8OPPclbdKXQSHSUCKMJwPr2LQnB2dlHfO+Th8wbg7OJTz9+wtX1LVEqDo5OuLhZ8+pyja7nPP3oKTFFiqLKQJ+WiBjQ0tDUS2ZVQ13UyDixupEEH3j89AjJFjdtGaZzQuyZjKEbWoSYUVU147gF2SILsHZgHHpu+2sodjRNS6lmeBdYrW6IcYMRM7rNRFsvOTl4gikK/LQXUKLIq8R3Fi9GabbbLaUpmIaRv/6r/4XF/H/Fp59/jykKxsmzWm+4W8c+evSAq9sF/bhmdXvFcnHMYtGwWa+ZOosxmuPjJ9nOpBnBBw6XmqpoKEzDbmdZHhzRzko262u22zVKg3MTZaURMeZwo0ITQuL7n32fpio5O3vAzfqWcepARHbdmu12xXZ7Q/QzLpB0U6BeHPP5D3Pq29XVNdPex2ocJsbNlkdPnpB8RKaM9RS1JolICCPjeMuLZ79m2q2pVcnueovbBZIzpKDwUXF9vWW97jg9myFkwe3tmnEnKFXkcDlnqkzOO5EGoRI+OIyp9qvQhJAVPni0qmmbA1whcS4Lx6QMSDVlQWecEOqWqtlyeOLZ7qZcN2WgHzbZQlskYmw5PFAgEv20oWkgJouPa2LcoMxA0ybcPt2x1AZUor+4pN/siH2FNAo9qwluxIc5lTz4z6rz/6jktX/o429btebV0j8UZBNJ9yf+90/4IUTEdywx3i/4AO/7KN0X6Pea0vtfe8dUypkIUv19/ODdfYpvfV7sEeW0nxbeX2HFyP3HIQRCcO9ynLmbQtSeHZRwLqdf4QL43Ci8IKefhWz6RYCEIgl5D9rHBCSN2D9HN44MQ0fSmhjyqEiIJDzawNFhyeHhnLIShDQRvOPq8poUpvsG0jZ1Zt2EgNSR280VQShcSChyLkRhKmLy2ClnN+OzQC8GR3C5URRGUImE9B43RpLItw0hMU4eF6EbB4oKhBpJwqFNQMmEz+Jv2mbJJ59+TlFV/Pu/+kt2qw4VJEJKdEps+xUpmez5NE6YVKFUiZSaeT3n46cf8eLr32JkTV0Kfv43f8PU39BUki9+/WtishRGMw4jKQnKomDT9xlc1wWr1SuE9JycPuajjw+5OL9h+fSMx08OmZLi6nbL4fGCoAtOn3xMUWpUVRFQTMPIom5AKNp2zqw9RhJzfOOiwhhHWU5o7XFuiw037Po31LXGqQo7wThYQPPsm9/Rjy0ff/aHzE8e4OyE9REZKzormZIljh1THTk6OMaUmpgE6/WGeX3K4ALb9UBdtrTtnIODA6RUmKIguOzaenp8wtdSYbTm8uKWse/RSiGqisvr17x4+YrTB0doZZi1Mx49PGO91Vgbef71C9THCwpZI0LEJ09bL+h2O4gFMVh0YehHi9AtsqrAaKapo9vmmM6nHz1lvphjJwsBYgpIpagqTTtfUJqs+zk8fUQ/bJBaIJ1Gx4HDtmS72nB1c8V2ggemJaZAUVa8ePkNr1694OlHTzl78oAXL16y2a0oihKpQBvQJhKF5ZuXX/DbX/4vuHHNQVvy6quXXL5cUakl0Umk0MzmM/puZLPtOXtUUFYt8/mS3eoCZRLTIBg6jU6JoGombynvBK1CYsoSrRtimJgmh/eRpp5RVxqlso9UVQjKQjCMazbdK5pW0ErH0bFmeXBIjIHVes181qB1BSiatqFqNF03MbobhArYcMM0XTJNt0z+lqIWFIWmQqAGwba0jLZjch31rEA3UOkZRWneOU/8/3n7R+Up3BdO7grvO73B3dcEWc0r0ntMIJEtK+4VB3croxhIIY+yd6dy8d73fZsddOdHdDcsvPvaP+QZfl+wpfzW99+td+4aU8YH4v5nSPtiEpjNZgA4Z5EqA8tKKULwjPvMYJEiKWZn07tnHUJAaAVSEZIkBMClfIpLoISBkAg2kEIG6RFZEmgj2BgwVaaUCSLOWULwlKVBkI328BMiJZZHhrOzA7SKeJc4PT7iRz/8jNcvnrG6vSCGnOvg/ERMDmMSLnQgPNZ7vBeEkH+3SpncoFK25PXOMU0uh5CnQFWQR2kviMljlGI3Jqq5zupnm90zrQ8omV8fikhpFNMwIGTJYnZECpLtuueD5Sl4jUkVrh+4Wl3Qf3OBk4ZyuaSczUnSIKJAoDNelAy//c1via7jyYMjVJp48/oliomxyFPcbFEwmzdUdYkyhvl8zm4aCH5Ay4hjZLOzbIcLjo7OqNuGyb8hKYmILQdHDQeqpTkIHD54SDIKFGgpqWIJKTCfzShNiZY5XvTy5oKf/+1f4v1V3g3Hgc3mHFMNzBqD9RPbW0fVKCYHIlmurs65vAlc3m74k3/yz9BSMKuPuLq5Yd7MefzkU0iRuiq5vdiijacuFxhWhGNHCoJnX3+NxPDnf/5PKYoCITIGJY3ExsRyecCHH37Es2df8uknn2WBUwjoVjG4ie//8AcUheLq+gVDN1Dqmgcnj2iaJd4VyFTSlvWe8urwLqJVhd97XQmpaWYlsjCoKKjaGhlGun7Dy5fPsK7jxz/5A5YHB+x2A94FhDCcnp2hi5KEpGha+nGgrOeYQtLfBgbreHx8wicff8bNemB5+hRdLwky4KXjanPF5fqS7quOB7sbTs4eMKWJoiwQgBMju+0t55fP+fUv/4o0rSikZXN1TX97hd0ORCFwLov2sm2958njJ5ycPMCHxMOHj/hqc0VZ5XCwaeoRZYUqa2pVcXK0pCwbRutp2hkpJfrBs7pZodTEfL6kLCRSTvlakBkr2O2uOX/zjMcfHNA0Mx6cHuFcwXq9pTANQeW/kVKGYehJ6RqlK0brsnleXIPYElgRxIqoFaI0iKARMlKXI7OarIeKnkIofvj5j1mcfcA3b87/s2r9f/6kEEEquefw5y65T5m8t4DIxTggkUip92ueXCDuijzx25RVuVcNczcdpJizj+8YSfx9MPpOv/A+O+m7OMOdIZRC3z+He3tZMjgtRGY/OQHB57SyFDyJxDSOhOD3CW3s6V0SHzxjPzD1I9oo4j4akwTR+8x0Sml/amsIqsEx5lEjOpRIEAQhCqJP94l2UUSmEPApUEpFXapsZCZk5o+SQ0iEzLL6xUxyelQzqxIpTBSq5mB5TFXM0Kbm4OiEadqw293Q24HoA0rJvLpCIxMoEREEQgpYl9UuLqTczGLM04yPVBLaQlIrhdF5vedioij2ViI+TyUxgFYw2omjozmzmULpyOOHZ7x9e0G3XpEq6Iqalz5hMGwtdL3H2piVuBKU26GcQ2iDAZQyxGQwRcHtbeBoecjjJ0/56re/YBxHjHAQE3ZKjL3n6tJyejrj8EHNze32Hli100jZtAQXGDdrmAInR4fUdcnx4ZKt24E+ZrSGTSfRumIiU4uNqjFFDhPSOjGvEgyX/Pt//6/5f/+r/xu//NW/57/4Lx/wyY9anOyIMger7PqIjA0mVhTTEavLnMwXOkVJSXCCcQjga/qN5WT2MX/y0/+C0+UDDg8OQQtevn7B6/PnHB8dIqXg9eVXPHn4GY+ePqLSC5pmCShSFCAqEgGpJcaUfP6jH9MuZ4x9z3q1wnrB1A+EFKjbEjt2BO9o6hmPH/9RXl9GhSkO6LcOoSLjtCOS7b+lkITk9tYdnikGihhpmgWJke1wxe3uNX/85z/g8vKC12+/ZpoeMmsOMKZGyZxtvZgvSSmxG7YUpiSlvJI833p8rFgP2XJifvgQIQpShK7fIcZEN9yC9gQG0BaftvgYcCHb47z65pKLq5fcXL9A2o5lIZiu1qSrK57UitlSs1p3KFGgaJnGkW704CWHi2Our26p68w+Gt2Ua1MINEpxuDxgNj/k5PgE7yO3qxWFLrK99X6FfHy8ZDZrCGGFMYmiTqB6nN+w27zADWumTUMYA9YqyrrCxzXS7DMSxEjTRmLccn3TsVgsQY5M7pqiDMQ47qNAa7SoiBP4mLMzovSoOjMmPZpiVnL25JCzp0cM7ur32xRSCKT3aKekvQuSeHfKv2Pk5E+H+8J/d8JHCCS5aELm6Gf2S9ozhN5f4cR7pfNdsd8/k28/r5R4HwS/e7+bXt4B4ulexSyUyCweKVFKZiBNK7wTBJsFHhlQdsS4X6fEiFYq4wYu7O81ZzJDpqam4IgxMPQju+2GYEdcgIAmBIFMYL3b13iRMQMfiSkQUsTFCDKh7l7gCXRh8irJTVSFZHlyQikDOnaUJhBdR6klDx+cQoycnT0kRsdXz37FerchESj2EZ0p6tyMpMke/GWJ1oIQA1Nw2NGR0DgfSFFhx0QhJEeLBhU9KmVhX/AOlV8CNKUmagFFRdj2CKDQhu99+jl9d8vt1VuapzWVUnR2oJ8cru+xDnbdxHrdMfQObxOLeUFRKVQRSHGHkgUaA6lEiBKlC5YHR6y3V/zi735Nv8nhKrNlSVsZRjkwDI6h88g0UtWJallxtFyw7m7ohh4ZTMYD2jnBWTaXF5ijllRbykIyBUGYDAeLx6So7o5ceDtB1BTa8Pb5F7z69c/53a9+ze9+83dsVq+ZyYE03WJHj9t2lLNE2xzgO4/dONxW882bN7x40bHdJaLQxHJOeTTHxBYVaj598j1+8P2fkjyUzIhjPoUfLz8kJcm6O+fZN78lJLh8esNPf/jPOD36GBEVWlWkJJnGsM/zFQzTjpAED588ZbtZU7czrA/0mx1HR0us3dH3G6qqYJocz756w8uXbzg5ecxPf3LG2YMzUI6vn/8GFzxltTfcM4nZrKQoVKaRykBVO1KaiHpDHy5YFnN6+5bb9Uu22yuW7RkfPvmcqixIIhe1oipw/YTznlnTMvWWw+Mzgg/sNre8efGGw8XIp59+TgyOt1fPubx+y+36DULD5DqubibWO0VZZp3F5Cy3q1tS6GjrRK0j4XaH3m05kqCTY3lYsFCB6y4StMPFEULil3/7t/z4x3/Ag9MHTN/7Pi9ffYW3awKeollQNsvMMjo8oygqdt2Gpm5RKkfyGnPKYm44Oi4pa8cwbIEeZRRF6dlcv2W3eo6OkW41cLvpGZ3i+OEpo1+hygktLQTH6M4pTaQwJUo5ouioqh6pIrvdiLOSg8UTZvMzvHOk8RaXLhBlw+w0ossGYWqS1vz2659z1X/DaHe/36YQYyR6h7pPDUr55BkjIuWP76ic8h+gmd6V53fV/N0JP4tBslfSXVG/Zyfx92moxpj7x3q/KcC3Ae473YEgKw3vwGPQaCX2Fhd5Ugne4fYN4f31Ut6rZ1bSMAz0Q5cZQfvnef/DEHG2x3vP+vaKvtthRMLIiDGCcUpEn9XHMSaUUGgjkCrv5FPIJnMICVESvMgME6MxQqIFHLQtDw6XLBpJU8DhrKBbXeLdyDDs6Pst/+4v/z/UtYEYKLQipgJjCpyz+KgYp8ThyTFu7BntSBHJY70yzKoGISvaInLx+hKlNUZlJ1tn84gniDn+T0LdFJhSESV4JzCFoK0qhA28/t0ziiKBm3j+u9/Q9x1GAESCDYydZ9h47BCZBig0pGgxoiR5mwuNLkFEUnQoHZAyUR4vsXbNenuVPaZixJQVVVPjfMJuHS7AejXhwhtmy4ZHTx+gdI1KkbHLKtP53OCcxdkNzt2wGyoWp6eM3uDjAc2iYIxxP/UFkrcIb3n51Qu++O3PCeOGYXuLigOGHQWJRkrUlGjaBu0Tc3FAVJHXq3OuXq54+6LHd4rYRaZgcXrLZhMJf+r4b/7ivwHVkJIhSdgNHeu+5+35BRfX5/T2luVxyTcvX3BycsqrV8/56Y/+gqKQJB9wroOkCQE0Zu/dJZhsYBq7bPMQeoQVaAMhRPrdACRm7QIhJEZPHB6d0bZLunHCpTWmAGczGUBrjfcWcEilWe8u6fs1EFBbyayt2WyvkWrD1fVbFgeSuNCsbl4ixMhqpwjpiPkQ8eESbVtU3VKWkpAs19tLXr16zvHBjJP5MeVc0fc9r66fM5+1bPtzvv7mF1i/QwvJOHZ0nQeR7qd+IQSHh4c0rSFNPeN2C9uOI20oK3DdDlUo9FFDUI7z1S2iCBRlwbp7mxXnpuTDjz/iJ9d/xPNnX7LdraiaUw4OP6Bpz9DFnCQDSMnB0QFSJMaxo6oaiiJgTKDQiXK53FtnT7ip4+3bb+j6DXU5YzeuOb/eMQUIxZaDY8HxaV7fTjtJDGNuAtqT5A4pY27CVmJtCamlrp/Qtk+z55J8Q/LQHglKY5gvjhh9YjvuWI9r7O2Gov6H6/J33/5RQPMdbTSllBPK5H59w7eLtxTvLLHvbvv+2/uFO8aIFDK7hu6/967Y5xXQdzIZRJ5Q3v++u0L+/oRwt1q600jcqxDTu+aRAWMPKe/QrbVYa5FS7rUPAWKmwYYQ2HVbhr6nMNmCIu5vJ1NE4rE+B5t02xuMktSVIo2OJDx1IXFJgdGwF9TtSVX4AMLrbKMQs5KZ4BFC3V/0MThmjWJRag7bkoNFxeGipSs1y0XLxcVbnLes11d025TBzXEiyUQzXyJdwWQF5XzBwYMPcFdv2e3e4CYP+2apkfxX//V/zbNnz3nzzQUpCKz33G4tlUp4KdACSgN6H1pjbY9NERsTpEipDKXMILEbOpR2uGGC4DMbSurcnEMOBVIi50FoKfCTxZeRssqgdqkFQiVkoSmqgiAEsjI8/uAU2xn6TcHm9oJhsiipSLJAqAJtIoUu6TYD2/VEtxk4fHDA/GBGjI7RTni3xvmO5aIghB27XQdFiwsVVZMQKTcClMwCyziRrON3P/8rbs+fMZsllnViCD1BWupZYq4K6lBS2f2FtRLsLnsuv1zhB1jIkmVbcDs5eiR9VCgzYyarnO4WAqqoCckhdZ5Q6nnJ21+/4eRszsnpCY93j3n88BGHy4f4MNINN2hpcC6wWQ8cHz0kpECMcg8sRqwdQHiECvtc4JxnkUJW6Auhqao5Dx7WHB7nA4LShq4bSDvL2Fu2m47ZrMEUgmmaeP7iNcN0i09bhLRY11OVBTIJpLGoOObDYZToYqC35wxuxu7qijeXX5KSxDQL6vlJdnoVmvPLc774+u/4TRh58uiEopBsdxu66ZrrrabfXrDr3oKIOBeRMpvdsSeiGKGYzVpqZZFTYnt5hV1vOSpKlrOGMjq2U48qBBp4VM158HjOm5ue6yExm5c0bYkpF7y9uGTXjRRlQ+UD1gWkLpl8pJUKo+U910WIiDYCoxQpSiQBLQVV02ZL7LRle3uOG3fUdUldz6jVnKsN4PPfpJ2VHCwLnHXYXUddaYp6ILHLE3kzR8iaEExeU9mW3cYglCEIDb4g6Rpdz7OLbNR4odF1iRcbptSh5O+5KSil7rOMhZDZNfW+iGc+/b34SyRI347GfCcI+/ssprspI/EuHOKdhuHbrCHgvdXQu4LP/rZ3U4PWeydW+a5ZaZ1ZPVKA936fr+xJMe7xg3B/P1nN7Ak+ME1TbkoxYrRGikzXDFMGp8tSY8gUVNtvKGSgqTWFigyuQ6YJUyQU5TvybvB5FeV9NuBSCplUbhQxEWLElAqjBH7aEeNIPVvSqkCjI2HqmAZ49OgRp6dHjHaLHQXeJIIbGbotdVli6ophCCTZYFGY+pTbTuCiZr58wPb6ijAGNILBj/z1v/1r3ry9wPaWbmtRwEEpKZTZB4lETCkwShD2zdAUGRD2IZH9BRO7zRptAs0+L7iqFFIpnAPnHSlJlIocNDVCGfqup985lIi0jUGXCa0SplRUjUFXiqQUXnvquuLGJtrTQ7Y358SQYw7Lss3W1CEhhEHiqLRkdzOy217w2Y9mzA+PkaLDhSGvC6OiKhe4OBKcQpuSqiqIcT81BokxCmd3rC/fwPCWQz2ifEddKf7kT3/E6nrL3/zst/SXWx4s59xcXpHwXItrbi92jFuHkgbpJLafME7TiiKr0YeJcbvj+Ek2dAtE0BC1o+t6Th8f89//7/87rm7e8Pr119ze3uCt5fiPT3nx6otMH10uUFrz6u05Y1hxuDij7yaOjh6gC00za7LNSioYx55hHNGmoKgrfEggDXW9QE4epKWumixIrA2EAhaBbtMx9ANF0dC2NedXO5QJDOMGIXtQA1ergVLPCC6gtcjN10dMXSEifP3yN8Qg80pEKuSupe5vqetEpWqk7Dk6NOy2O3bdOaJzDGNPDNkpdHX9BlJPoRXBWYRICBFIIlAow7yt0DrS3V4Se4u96bCbic70nK/XzAqF0QG9P88KJVicnPDk6RmdPMLMP8ieVspghynHgmrFbF4zTCNvLr7BS8/iuCHYhHXZ6bipNUaB1onCSGL0TJPNZ+YE3nZEu+HkcMloE2V9SjN/guOMbrBoEylVwvUT4yBQ0qBNQuoRVUCMAlUoBC1KzigLjWZBVZ4QU4ENA85Z8BNGCYQuoVii5QItFYO/xYYrhmH6z6r1/6j1kVL63f5e5FN5JGXQOb2jeWZdwbcLP/CfbAoIcqj1PU01/IOsovvbiveaBu9pHu6psdyvpEgJqe4+JzKQHQPjOGR1aQz366Ci0Fhrc4MLkEJWoMaUJe6lMQSZAzdCyMBYYfILQqWELmWeCEaPjAkhEoVOyJiILqJ1gfMBKQVlkcNrhiHjCumuMUaRgXolCG6k33VUByWHc0NbBDQDMkGpDXbc8vZ84OrmLd1ujdECIbOVspt6xslSVA1tu2B29BFfPL/msx/8KT/8oz9g9fZL/t2//J/wU8L1kWHskNLy5vkNScA0hcx/LjIw733AGKiK/BhJJOpaoU2BKipGG9lsJ/q+QzcSKRNKC2yCJCWmqPY0WLBC4qRkwmXX10LDAO1cMJtrikKgdPaPKnQipZHgNhgzR0hPih6RBpKP1JXGDROV0oy7jm4zkCIURaIpK6L3aGWwLvL21TVSN6CzsnOxKInJEoViPj/CJ6iqGcooorAYpfeT6USi5/z5L9HuGil7tJio0EzbWxbVjLPDGf2m5/LZJavNiqqUFEpRUaGkZtdPBJ8Yd/nwMUZH0pJuZ9lsbzh2O+RMo4sSHydc6kA7Lq5fcnx6ylfPvuDZ179ls7lh7LdIGVDScrN+zm+eveH0wQkPTp4wxXN2UyKJktHtmEaLtSMJSwRscHgRkCqCkUit8Aj67QhJo9UMJUp0IQjegs7ZB1ppxqnDTYqqabDO48Ydk7OY2mf3VxkY7ITcH6GjTKAgEnn06AE/++v/yNA5irKirkpmM8eHT85o1MSw3bA+v2F7+QbnesZVxirKSucDmveUYmLeFGgl8CrjPEoLKtMQXaDwgduLW1Lw1MkwL0vG0lEJQYEkOUdRaqTI12AiEqZrzh6d8fHDz2mOv8fhfIY0mocnR8jkePn8C5zvaBY1O3tNuYD1do6hoes7jA4sZocUpSL4ESk8UklEAjdavM+ZKUpKTFmBUBwdPaW3DWVZEIi5sbmJ7naNtRVVGynLCMLtCTEGyQIlT6mLQ5bzkhgbjGmJCqT0oPLfOYYJoRpKNYd0SEoFpVkgaBi6699vU8gpadmWIcvU7rihdyf3d3qFXET1Pch81yy+rYp+TyPwnm7hu9iA+I6baYoJ9V5Q9V1T+IcM7/Jtwp4F9c7KIu4jNLWSCH2n0E4463A2n97vfmYhUl4XkTUE1lqmaSKlSFM1FEoSXZabG5VoKokb8+dihNJIkoOoJC6K/e9JobQiJLXPe1YEF+8jQLVSROEZ7EBdwrxtmM80dZkQaUBLzZPHj7m+vaEfBqzPgreUBEoXyBTRpgQxsdkM6KaiPdR8//s/4Q//8M8Az9e/+Yqvv3xGGgbSaEkuoqWgLgoCYKQHAtH5bFAWIm2jKUtBWQAiMJvPmM3nKFPxzYs3kGDTjeiqpCwlU7AoJziYz+hFVh2HQtMcFwyXOybRIVMOh3EicTAvOTk7QElHMzM08zm9S5xfX1G3UArN1G2QUtAUYIds3jeuA1F73BSJNiKlIDhHEmKvBjVoZdh1nucvLjk4OeLR0w8oCsnkBoyGotQ0pUSXDc736GaGFA7nJmIYGbdvcOM5B4vA+nxkfqhYLpZcvHxD8AUEgU4Fm8sdxIQWgqYpAY0PHu8Tkwt4BAE4frhg1IKu27KxV+zcLbU09EPH4Ha4sKbvJw6WxxwezviTP/opX/zml4Dg9PQU5wc2wy2JQNQrXl2+4ZuX/xFNy8HsCQ+OPmU5m1MaTb9zSJXureyDiLlYy6zaVkKjq4YUNCJojKqojCLu8xCqqqKuS6pasjhomS0aHj96yvWNoIqSdf+KXT+iDQhGlJYEl3GnyU5sN9ckFIPtEFrRNAYpYFbAePmKt9dXGFFwXLXMjipevXyLVgEhAjpm6/spOOZltp7XWjFZj0fhJosKjllRc3N+RegykaOZGUrg4GBJsnliVMKjZCQES8KhCkN0G15/82v85chnf6CYN4Zt5zFFwclS8e8uvmayHWVXYWYz9IvEg5OHJC3zawtPjNN+C5F9y7J/WZHDrbzE+ogdEoWWFOWSkAq2nWO9GUiiyFsMYTA0TFNBUgPtwZyq1VjnMXoJ6RSZHpLSjAQoVYAM2f2ASF2U4EuGrqcuDUrWjKNBypqibHN4GOr32xRijCAzMApin8NM/ngfT3kPJX8nBOd94Pf9qeFeS5CyU+h3G8A94Pv+80iR5OO39AZ3z++7K6qU0j3l9V6PsF8N1VWJMXm/be2I93md453LxnMxopTEGJ0zGfb3H2M+ZRSmotD51IGIBD9hbY9MnqbSeG8QMSADJKlQRuBt9h5qmpqqqRiGAesTKbh3gjkhmIYRGy0nxyUPTxuOFhXLRvHk4SHEidv1BeJFYHKODO2YzNaiQJoMoG62lnGM2BiZFYJPP/qMX3/1lv/D//A/sLq9IAzXKG/RIZGmSFtptDY5vUuAw0EKmAp0BCUkRaHQGowRlFXJp59+wnx5gHWRi8sbjB7xAqIROKnwIZvpyeaEMUDympQKqnKJaBta5Xj4YI4UPTFZmgqi8BAtShoW8xa73nF8MKdezLhdXfH69Qsenj2glIrBWYK1pMCeCiwy/uMTRM+YIknmSNiTB0/oHdxsd5yWRxTVMSF5hK7RZZm9ZkTPZtsRpWVZVijlMxkhTpSq48GRhElShJztW2qDUZHr611u8EkihaIsIPnAZt0jhaC3IfvalIaPPzqhauc4Jbgcdxz5xDfXv6P/HXxa/hFVe8QwbolxYjavsh1zCjx6+ITPPvkB6/UNHz79gOwPFrndXDKEKya3xo2RUswQwXLYtvTbt8ybM+ZVweXVOXVjMCkhJFxfvMKnwMnZQ0ZrEcJRmUPmbXNv9ucCGCNJKuFCjzKRzW6kXRZ8/r3POLyZ8dsvfs6sOWGxmNENN2x2V8SYMEqgpMr4joHzNy8wCkpdYWRCpIgeeqbrW5SpOTo65bMHR1x0IzfbS5pWE4Ln0ZNHmKLkdrVldb1ls9litKLbdnsAHIplxcHZkiBLRLCklLCrDXVVslgs2Vl77x9k/YguBVpCDB4/9hihmLbPef2N4fnXP+PiYkNIEucmnp61rHeeV+eX6HGkrudEFxA6MGtLBJ6+u0FhKPa5FUIUCGlII4RgcLZiGCtUO6OUM66uV7x8s+LN1Yb54jAr+aNAa48qSor6iPnyATEF+vWO6Fvq8pDF7JQUDSlOJA1JeKQokV4jZUtbneCHyHo1YcdVNtIsPIv5DJE0OrW/36aQmUDfLfaZXy9k+tZpPe2l199dG31X5fy+gEz8p1ZF9z5F7wp9DPE/mYfw/mPkm9wlrwF3thckrLX3O3LvXDa6C+9otClllk2Rsmmc8z6P4SFQFQVVlbOMY/CQAm4amOw2nzqNIgaBc57kfUaSE/iQvVLaxYKDwyN2245+ithpR0iB5B3eBXwIVHXis+894INHJ4gwEVxHEDkgvBt6preWqq6p6paUIs5DP/S5YSIYpuwfNFjH+sUl/+f/8f/Ezcay6yPJT9Q6oFLEICia7JwqJbnBBYuWgdJItIgID/NZCQRmsxZrO46OjmmalouLC65vVuy6HVImnIXr1cThcUMSNUk0JLHERk9KCiGyYvfowQcYrQl2hR876qbFGMs47KiLlFW5QoD3HB8e0y4XDP2Ojz/6kFnbsrrZ0O8GxtEBAhf2DDgyeK+UQpcaWRTU7REffPx9eidIby9Q1RJHRSTkZDRRYBMcNBVSKyY/MO4uKYs5wsPYrwjuiqa0IAPmpGR1PbJar/GppGxrfBAMXYdMEaUyX966TCueAsi24NEHD5kvFozeI0uFqhTLak5dFlz3bzHnM378kwOWqmZ1OyCEwk6eoZtY1sf8+Z/+M96ev6JpDUJ6miZxeW3ZbK4JaYORhpQCCYMOW/qb19RBo6NhJhLPv/wNw7jGx5Hr9RWdH/DjJ1TzY8ryFCMUMVZ7MzyP8yNCBXrXsdpekMSE8wM369d88MEH3N7esNv1HJ0sWW87pmHC2R1YT9RgyVYuta5RRlAWLVrWtPWMpqhIl9dsn79G1TVXl1fI89eIMPKkEhgdsdHxtKmoqhllb9nt3nAsJYfLJauQ2KWeKQSaEGkSOFMQlCT6TBQQwTKNO6LPBwTnHWVp7s+uUuXXSrQjQjk2F5bJSVSqsaMnJVgWkvZwTr/boesljw4eI5wglYGyzLHA47hDS4WSNYUp0KbKVUY4bFCMrmS1VShTUrUFl9cv+O0XX1LN6mxFUXq8iJlQgkfoGbdrwXZnGQZDdIa20pmAkiAmuTfXBLwguQJUS2U0toSr3TVdd42QE3MpCEHmhmyK/6xa/48CmtM96JvXOvvt0V6D8O1p4LuWFt99+24hj97v3TolWut93mzkuw6ngjydfBef+IdWT1JmEPtORyFS2sdXZj1B8FmtLCX7YAxJVZVoo3D23XTgvc9ro3FESUVVVWij8W4i+ongBuzYkfxIoQq0VgwxMYyWZC0pZEqfx1KUNUlqqmZBiBohVoxuR4i58UxToCwEDx8dUBVgTOLJRx/xzddf8ObqGjcOPHzwmJQSt1fXOJs4Pj1DKM1kB9brFU3dEKi5WW8ZxymDetZigqIVCaFA+mzHXWmFELl4SRURGiot0TLRVpJKC0QkU1yLAinh408+oSgMb16fM9gs7Y8hEEMkegh9Isw0pqwp9JLo2/z3jXB8+oBpshSlQQK7YWJ3vUaEntmBQQmBH0fGFLm9uGS72gKZ7VUVmro9RKmCGAv6PrLdeYRxTEFgbcDu7agKKSnaBaadUdVHnN8OeFFxcPIB666HzcRsXlOWJdZH3OgohadpDMlv2W1uqGcOXKIIPdauKdUIIuNF84OKboDHHzzk8PQJ51cr3r56Q5h2YAcKo/AB+jFQ6JLjR6c0yznr7ZYhOoQqWfkNsmiZZCAoeHP+DcenJ3z05HuEKVCXCxazY86OP0CEgrOTTxCpJjExTLfcbFYkFLNyhrc5yKk1grlJTLev+cXfPef04GP+4Id/zJc/+xn/4l/8P4GJH3z2MY8/OmPQke71bwiHD7gOrzk++YT2iaHbXtFWFSHl6fftzXMGd8uuv8C5Hu8dyowcH5+S0sjLF2/wYYeUI42u8LaHyRM9iCAxWlOJGhVqPv/4x5wePiBZx/Xm7zC8YR4FdQjMbI8IPVp6Nlc9y6ZEbTbEnUNcr2jHkcOjAw7bmiMJfVNweX1DSo7x+pxKaD44PuTm+payLWgqRUySlAw+wDCFTJvbT3Q6iUxZj9CYhJ3WFEmz61bUpiZCtkwXhseHB9x2kRKdrWuCI/qISPkABQnvhhyva0qkLmkXBagC52e5IRSJSKRpK55+eIgqNe1SU1QSEWW+dqyhmxQXtxv6zlOaBaWZE2mYLCQyGUaLzBpLaS8RCJGAoiiPqJrIph9oG0WzEEjt92vp33Oewh2AfNcU5J3aVoosd96fwvPX37GG3m8M/1CTEEKQQsL6fdLWnjV0X+Qj31I1S5lXLO9PH3f3813ripQEWoo9myfsYzPf2UbEAEqqvbAtorWkKOo8ORSG4Py+IWQMQSuN1vk9r50s0Y7YoSe6KfuhkNBKZYtwkXEEkSQxSUKKTM7R9SOTjSQKlK5AGMZph7eBspQ8/eCYx0+WNE22s371Ou9sjamoZiUnZ09Y3d6gzA5rHZv1jtnBGYvlDCUbjC7YbtaM9hJxdxzyjloJtNrbbchc+CuT0/B8yiyOpqloqxKFo5aBplT3NN1xHFksD5nNctbryckZ33zzgsvuluBjBqIjjB521wOySBh1wKhBqRqjFZWuWN/c8OLZl5ydnWBI1FWDComx21BgmdcGlQTjukNHEM6Dc7RVw/VqR1G1FEXL8vgM+faWplDYwebQcz3gfUBWDc3ikHJ+SBIzJl/gk2EYAlMAM3lUHZEBJhtI1hJ2azZixTi9JfqeuYRCGEwaUdJRz0z2LgqJw2bBjJJq3kAZcHLg4GyGTiXJDhSmQKmSMSikqUFCP45MwbEa1txuBsZSsiwbhEgoJTJ7RnhEgrOTD9CiZtEe0ZQHBKtIKnAwE7jY0bQ1UTq6YY10G1zoOJgXfPzkCYfVIW4r0LuB7vIV8clTfvjBKf+x1cgY+bAuketr+rACE5mYeH7R0e12nD14nKcuKdh1t7y9es22v+TwpKGxMy6vVqRx4O3F7zg8qvn44wd8+bsrrO0ojQBfgI+IFFFJkoIkjQqZSp4++owfPf4jtCi47S4RO8FSF5QhMNOSGZ6ULNJbQogcFYrKWfreYq9XNDFgpo7xetyroC0NiZAEM5MPfj5YNJF5XTBftlnB7TqEyGvR0QVUqSiF2eOKhuViQUiei6sbkkqUUTCrNF3f56yQpDmaHeFDYntzyXW75EQ/REuNVp660tnnSArMPjwsAVpX1DNNHCPz44eoNIDbcnZ2xtHxjKQAZfBJYUdBt5twY8fb17fsup5Ze0zdLGlmJ8wXh0hRYqdAihKSQaIQKudh2BGGMaJNTdksaLSknlWYKhtfpqCQ/J4nhRjjHip4nyaa8QBBtr0Q+2mB9Peng7usg7u3vSoBIQRKK2T2T75vCu+yEd41D5kd6e7XWN9tCO83hfvHDZ7g7d7ZNN0zjWL258hFk4hUeu9PItnjzN+6P60VyhiEyDB78I7oR4IfcWOPjB5Tm8xuCtkbSGvDmCZEEkhjwE9MdmK1WrOYbxEiJ6mlkOHH48OKo+M5jx+fsFxorF3hLGzGkdlswccffsTzF6/YdJHtxlEUM0a/YbNZg25o2kPa2QJBgfOC07PH2GGDtwNlIREBZpWmUoah79EqkQh7PEJig88nQZ1ZH1oJxrFD6YwlVbXB2pHf/OpXPHnylGm0DH0HMVIogVDZryln7AZWmy3j+JrloePs4WN0o3j59dcM45o4brh5vaNUhqaQGGnZrToK4SlSzt4d+xFZFkDC2oCPkTFIpKwpqyXKVwRRM6sbnM1CKzk5ko+YuqF3CSMrIiVRlijTUlclVZoQKr8ehC6pTIvwnl/8679iXo8cHUpk8uxubqlNQdfdYIqJxaGm9w4jNajAbN4ga4WXluPjBoFg2K7RSeV0NlGAFwx2n5CXAr233IwDrpI8+PCYTjhMVVLoiropGP1ANw0czk8pixoS7DZbtJgxdI6qXiKcop8mRCxII6hJ04iWDw+P+GA5x8REKBVq2XJrLdPuhmAH6jCQui1vf/k3JDWxTiPFSUV9dsaciv7qOVdvf8vx2Yesu4EvvvqSYdzRzNR+DTNmmrXvcNHz9sUXnB6fcDKviV1CdI4iGMpQUxWGqqjwQpAoOZg/4k9/+BdoWxBsZLjq2Vzc0rpIWSqePjpj6q5yql70NCXgLdJ7hI9I71jWipPDFiGyS6kbdiwbwW5IhGkkCUFKiqaQ7LYDQsHB4Snm4Zyrmy3WbSi0YBgDIQyUWjKbtWjdIGNAMeBdvu7HbiC4CSMFEBjHDd//7IdYWm5v36JrTVUdomVAK0HTaBAp60tk2rtCS4Qo0abFxRWXV+f48ZaDhWIYOkxVoEyBNg1G1TSVAHq2U2J3saNpDVJpfPKsdytKU0PK6nUp8opaqJjFlSngQkAXGqVLdJiIITFNDoPBCA3i9xyyk/AIJEIoBIKwB11F3IvI9qsWZWReV6QsnkkiM5akZJ/Ns28HUmbnUSHzJCAllTH3ayOtNcg9Y+m9Ap33VQLv3w/U2avAyE6mpdkbu6X8y5rGDut8zjoWkoTIz09CFFkAJ7TBaEVyDjcOTNOUAVfvCESkVnlF4hwxeFIKyDTg3RrBQF0b2rYm+olpGEk+YXTWcNgQUVKCEsiQcKNj3PaIJBhub/D9lk8+WPL5954gRBbkCBxaB1LoUMlRqQVPH3/Ey29ukWLJR58+5uriGf24IYSR9facKGDW5lSuyUnq2TGmLCCOpDAxb1qSTyTnCdoTvUXrghA9SiZmRYkiA4NSegZnEclz1NYcLBeMvaXfTUQfubk8x7vsC2XwKJNzIESR/9aji0w+EcPAanWJMZJTeYyJFq0gugHsBAJUY5A6YghEB7tNRCRHsyxIOiGalp6KwVekumXSB/gwJ+glxfJjUpwo9IRzU7YpkXC77VgWc6agEUVNVBV1u6SpSrTybHc3tNWco+MPGFOBIDHR8NUvf8dHHxxxPDdgLfPaEvxE9I6rcQcyYUqJdyPd+pKWyOLghNnCcHNzy+Q7pFZUdckwWayzLJbHbHYd/TRxE0ameUV11NCJgFABLUeKusTLgTfbt9xaxU++f0KhCqR3KOHwyYMpEaVGyJoyLpluHAtf0cpDlB15rBRcPieGxNXrDp8ecnzyMbpI/PJnP6MaNiyl5UBDkp4HRjJNE/7qkrKd4arI+W/+DbvVSy47i2kXPDg7pJCR3bpjs7shXPfMokRLiz1/yes3LxAOHocaMUXiekLHxEcfnYHSdCFx9oPv8/CDz4leIwP40PH61/+B1N2iTeTh2SnzRQOpxtotSZJtaPa26U2jOTjIEZoijOgClHE8bGtCEtzcBt6ejyDAeU+MMltPCMX19S3DJFhtBozJjCAfAl0XOT5q8FEhTctmvWU3ZXZhO59lSx5dIgtFXbfQOYZuxec//JiRmkFUxJDXic5P2DBQ1UWmTAuJd5I4BoSoKYsjqtLy9vJ3vH39lrMHS4wx1HWF0YbDgxmzdpGbbgHL2SllecPNzSXOjShVIDBErzhYPOCTD7+H0JHkc/ZJ9AE7TVhrc82hoNEHBB+IXuBlQOgJaX7P4rVcgPcn9/Rtx9NcmGVeI+0nh6xjyOZyiPfYRrxLSmPfHGTMK5d3ATX5frNb4LdT1ZRS95OIc46iMNz5Jd01iHf2GYrowGhNjJmjDZKyKrOCNnrifkfnvMOPkULJHKLjcrCL0ZpC68xo8JmiGLyD6Il2gxvWGCmoCkn0E8RICgGRoFCK0hiizxbh+Xnln3HsB5y12GnLyZHh808fMWslwSemKefTFqXK6U7OY8eBVy++IcWJy4tXaP2Ijz76mM36PFNps7CTaRpJVJhCE0ePlEUmEAePd5Zut8a7XJjbRc18PsuTT7CQAjIGJAk3jfjgaCpD3WbB0tANGCN49OiQ3aYnBYePYR93mht+IQWqFCgjiCngYqKbdlxfBWLsKUXE2x3JD5kCKiLRTfiUmwk6249bp/A7T6kN2kvKxQlRLkGUBFERaRFGI4sFrrvi8voGFft9cJBGFzWqaJG6wqcstsziRod1I4eLlgcPTlgul+z8fkVWzel9wZfPbrhtFYet5rMP57SVRPh8IArRE+KE1JoYEsPtJdubG1xM+zCZiCwqqEqUgKZUnBzNcMlz8+YNspnzwQcf4Ax4JkrjCHFg3FrGFNEzQzfd8s2Xv+Lgez/geHFELQNXlxdcrwMPHn9KITUqJI7KlsWjjyg7w/rikrBeoc2OpqwQQ0dIO2aPa67Xtzg75CQ+4Sh11oEEmZAhMA0deaEdGJzjxfk5o6p49PHnODcxm8/47OCI3715jh8DEk9hoFSghcp4nA24wTLtOg7nM8xuRz1fYJSiv3jLVVQ8PHtKKTXnt2+w63MKYalKyQ9/9Dnnb56x3lxjiuyGIIVEaUlKNquWmbLLrRtQpWK+qBFKsesth0c1Ccnr133elPp4nzboXGCzGZmmhCrzarisSmJy+CDp+kwN1dWMyV7QNHOKoiQRssWLygK2hw8PWXWObndFe/IEF3KEpnUjkQmxk/hUUZSznL2RJoYhoQ3IqCnNjNOTp5y/Pef12w2Hh3NGO2F0ZLE43uOJA5fXF1xvrnFuJOEYpkRVzairgijyYUPtr5GUssap73qmye5tSDID06garWIW5saE9wGl/e+3KcB+XZP26uX9if192uidQV7OBIh8ty/lEJt98U53dhnxvc+9WzndaRru3v1+p3NvyEcWo92tl7Legfe+J99dEgqEQqlEUxZomZ+DnfpsWhc9RWGARAqBSSSc8/vGZtD7PAZn/b2+wU8W7wbStEMGh6kKknOMQwaVU8x6imADIkZUSqQIMWqcjbhhh+8dVak4WJQs5oIUe4I3eG/37B+BHR1GaWbtjBAiX375H/ne59/n5nbF0F1w/L0n1MWcUEhGL1Cq4Xa1woXI4eIQoxKSYq/wjRTaMuiRWsPyYElhDEJFXLBEP+YwnkIhYrYRSGR3VhDc3m4YxxEtzJ6ZMhBTxFq3d8nNQUJaZnsQFQOlyXYCQgqkidxenxNspNSRWS0IHpQED4SUQ45E1IQosZ2nlAo/KsYby0ldMYmSWM7wwSBEQVlUFPWcYViBNpR6RlFBNyVmi1Pmi+NsPLfHpdzYMwlNIR3Pv3rB+vYtP/yJwDRLSJIf/8EfcfHmDb5bMdodr9c9bjtwtJCcHsHRoUEg8rqviJiiRIoE3hJDQiuD0BqlJGEYc1SmNPTrFRdXl4w+8vlP/5CTz/6ASWhG17NZv8UOt4Q4cbm6hkEgUqB784ZhvuTm9pY3z15x/nbLbPmER8tjCl3Tb9YcCgWDRUfLotHgR6ZuYD5TPD485tfPNqgQiS4SRcK0e8sUmS0ZhEqUEaRMVDLhvIVxRV0vefDkKR998IgXz19z/tWXfPqnP+GxEHQ3t7SthMkxa2qqoiSMln7dETYDxRRIwnO9WXF8esyDpx9j2prnL77AX11QSMXq/JzSdRTC0lQNX335a6ZxTVkpoh9yepuEqhRolfVDQnh8gFpnx2Ip86FDKUFRlFibKcVp79QbfWDsR6wV2CmBqkiiQpiCpBREh5AGaz3n57cEYHKBWihKU5KEz5nQw0g/DBw/NJSVpptuGG4doaxQKefERCJq1Pjo0VOkbQoQ2YajHwYELSkJHpyc8cMf/ISvvvo1MQhWtxvatiIEh/UDSXguLl/x7PUzRj/QNCVldURZatqmpTALjpanaFUhkqKscq5F4Rxpr9/w0b6X7ZJIKvu/hRAZh99zU8gWzu8K8vvF+S7NjH2xz9nFIu+ipcz0zj3dM+yN6bQukELkTAHJvYne+1iEUZpE3s/d+yHFiEQCkbIs3lFa75/He9TVmDOVE9niIveDTB/drm65ub7ETkP2S6lLpMw+9FJqEoLJjfQ+3Peq4D34vHaxw4D0nlldYoTEjTbjF84iEVjrGUaLmzwhQkqS4CR28ti+xxU9TTlnMS+o64iUFqMlQuSdfCJbGCtZUxYNUiS6fsXr118iZeTw4IRht2Kz6pjPH/DRo6c8e/mM9WZL322IdmTeLpBoonf7013i8MCgjUDrhPM9dpjyyaLSGKURCSSSmgYhenwIrNcdyXmiD1SFIMY+TxbsU/ZCuk8hklKgEUijMKXCJYF2mXGRqAnec3O53eM7KoupUspgfBCEADJJYlRsg8dtNizODjh41IBZ4mKJSxpFRRQlwlQkZajnS0waqesW2XmkaZDlHB8VQuVsZSkSIljGfk2pInZ7xbNf/TXHZ4+xQXEwO+CHP/gJv/zZv4OYqIxAS4lwkjQmQueRImNrIiSE92AEhVAUusBFcC5jNF441H4qvH77lvW65+zRUx6efUzkgBgMRh7z6Qefgt8xjTdo/wXnN5fEySHFxNvffsXLbke/2jGOgieHjzkQEREcu9s1C1Py6y++YVZdUZkJLQRqNNxuVhwuDzk7WIBzeW0I6FpSC4XxkUKL/dQOjRQkEfHRUiZJGNbIl1+DkRwNjqvz51z8bOAnn3/IqAU6BsrKUAhgHOk2W+gn5kohWp0xvJDwtxfoowM+fvIB3dvXnD/7Fbv1Ftv3HJQGiAy7Hat9zvBsXlDokhhsdllWIGRAiDyZeJPXyZOdENojlERJs2cVOmatYrcLFEYz2IidAiGVCKMRsmE95MnV+MisaXBJ4qeQcUE8Pjq2ux1PHz9knHak6JimbMR4c/0GM2+plwpVSUzlUCbXFCkVmXme0EExDpeMNlJULca02YMq1kipePjgcX5sn+NKITsmrFY31HVJINPO69LsXZlz8JUzkXnT0jQLpCyy4FAWoCRlUeC9y3hMykyo92suRZ6E7ww/f29NIYa7Yn3HLtpL2VPKsnaR3VHvvuv9pnF/H3vxGFLc30+eBNJef+ARSbyzzBZpDyyn/f/Z87Df0V/vHusOd/i2jYZACo0oFCJ6vJuwY8fYbdjcntNvbvB2QrgO12mEVEShMEWFkBLvIzG+mzyCC/tQnWyCZ5Tcm0zlHFo3BbzPwhzrAs4nQhT7KUHgpoif8s/R1oa2kWjlWMwqyiLhbE+MAbp0Lt0AAQAASURBVKMVPmZGymw+5y/+yX/FyxfPePF8wo4b7LRh2K55++IcrVr++I/+jKc//kPe/I//R4ypcO6a8/NzNuWKed1QKGhqgIhIEyBRylBUBeV+LQK5GUSfJx2pNEJq7BRYr3sKqQg+ICE3L8meGADWsmeOCUQUaJ1zJ6TIvu6ztuLDj5+y6zquL28ZK5kv2JDQEnyKpAAhgN87yVrn8kpCAZ3n7fmKxdkJXhQEaQhR5+Q6pUlaUTQ1ldBMk2NwnrKoKeo5ApMdXlPCiISftvSba2TqOVzUbM+/IfUrHjz+COsFT06W/KLvOWwMjw7nPFhqKkYK5Ugu5/Jqo3BjYn3rcN6htKSaWbwQbCcHRYGuKoqqRimHmzyVrjg7fUIlSyanMFQIXZEmiabgsFqw+N4p/+FvfsbZoxMeHJ7w8ndf8PKbb1g0DSpCtJ5hs2W5OOHs6IQ4jixnp+y6FSnVuN2Wh/MjNJdcvX1DF2dQX3H09Cecnjzg7fab/QEqIZJHKajU3lIlBkJKqL1x8HD9ivN+RZSKJ61iuvyabuZYijzFFkgUESkVShp68pRcGo3I0mamybJ+/ZxzpaidZSEchUnshEO7gEsBn2AYwBSCyiWaeUtKBXbsCVHsae+ZxNA0mtFO9DuH1omyqogp5WLnI7O6RESLwKC0ZD0mQjIkZUDPkCSS9wQjqJeHJDtQ64p+t2Xobjg6mkH0rDe3VEUWa1axwAmPV5HThzOWpxWq0QQNiETweXUuhCElhRQaXRiimBjHa3zokClQV6cIFBKYt0uGUUFRIFS2ueiHkX5whCBRssq56bKgMDNCkOx2E4/OWpp6gUwFWt55fIFWGYNVTudayl09jfd1WAqLFL/npnCXX3xfdKXcn2b3lsrv1WIhJfIuUSzF+32/2KuWpcpf3ztc7HUFezuMJJCwB5nDt4DkrI3IFFPIoFCeALJm4n0DvrzaUlkSP/Z451ApkMLEZn1Jt7ok7FlDdtcxhoCPIMsGYwqSVHm3KTOLKKWUp4EYEGQXUww4n3+u0cFmO+YQIZmpeN5nwNv7gHNZfeltwCjB4UHF0WFF20RmrcHtx8UQJdvtjiigmS3QRcHJJ9+nrA44f/MGP40M1jHsJowKtG3Lb774FZe7LcYojo9OuXz7lugnylZSmkCKAzF4YtAUWtE0DaasMWWJD5m6a63DW491ET+5DF4NDjdGNAqPR5CoKUgImnZGv+tQGpK9W49BiAHps1241Bl8Rlhur15xdbnB2uxK29S5sVifrY99AoTAhTxNORcQRW4Uw8UVQb7mj5/+hGiqnH+BQBhJs5wjwpKJHXZzTec8h2dnPP3kexTtMT4Z1qs1426H1oJu2+HtwCdPj2kLwYvbt4w3O3538RpZHtPveg7UwOePTlmUiWQ3aB3QApTW1HXWsWy6ieQH3JAYU2SaJqwQDElgmTCNROnMRKvahoPFATOhaUK+iAslcftUPikLTDkj9JLpWvPjP/+ntGXF2eJDPv/wJwQ7EUKgrGbEKOn7iapoePPqgk9/9EeU1Y/o1heIqefxIvK7n/0/8NMlwzCRyksefyR4eHTG+ZeaaBPGgBYJCdmXK8MJFHuSANKhZECGhCoMSUSqMnDx6jeUQlBIg9aCGHIQkxaSSpeklO+oKDODUBqNH3re/u636KLCrjpklFSJnFUiBX2m0OOTQI2CQKAoNKSGgEEJhU8D1ufXZXCgNPRd3sHHKBiHxGSzY4ARmnFyyHJGIOGiJsgCXc05OT1AVyVKwdGyIU0Di7rm9ctn+IsBoTyzeYlQjojAGEMhDM46TC1YHpUcnhYsTw4YfMC6xDgIgi8IwRCCxE0BISzztgB6QtplavKU0DqzjJyLlKbCeYFWgrIyGd/oOpryhEINtLOGqilRWlMWDWUxZ7k4oaln2DHsMdpw7/EmRGZuZquceL81iTFrGGIQCH7P7KMQwj1+IKW8d7K4K8opZodMIeW3Tvp3sgXJO5qplgohMvsIQGlFjDknAd7ZaYcY79lIQoi93cSdajU3BueyaZS+F7u9i928uy8fElKInIwWLK7f4sYduAliwHm7B8YT1ntCDhm+n4bu2LciZdwhBJeB20IS6wIpFZOXyHKGVgVaG6ZxYne7zicwnyct6xwxRA5PSh6eHfDo4ZxPP3nI+dsX3Nz0tPMDEoLr1YayMSQC7WLOr3/2c5azY7732U/5xc/+FXhJYySjtaw316x7z9X2hn/yF/+Mf/tv/hI7Rc5OTvjJjz7m6vJrYozMGs3BYpanOikIPjKO2wyeBhgni7OeaXTYwRFdJLhIsBBdoDJkiwvtmJ0cMWsq1qseoxVCOKIAsRcCZlZanuqUUgTv2N3eUmqIPlFokCqfAmMEbXKuNVISA0w+Z2soAUUliToDiyn57KtzP2gK5ssl/UoitMbGSD1rOXv8BFWURKGxLmGKko29xqQMoNtpIHjP1e01baE4Wsx58ew5b75+hh0Dp5VgzpYiBLRyGAEpZv98Zx3Ox5yPkQqknJBIQpRElU/J88UR3//JH/D65XO+/O1vKfueRap49ou/49NYcvikYnQeSYXULTKVVMlQlif8l3/839Kok+w2G+H4dE70Uy4AWgEKFyWb1YYhCbZeoc0Z85NTjO+4eftzuiGSEBwfLXh28ZKHN1fMTM2iWFCELVpMe/q1IPhAyIJ7wv7ayqLOzOTTJhFl1h2IkDFFpCISSdIQoyDJvC5MOITviT7kv6tW1E1F8JFuvUHHhMvuKUSyU69PcLsLtKLEd5LOJg6WNYWW7KaAiIHNZsIOmdWkBJgEcYAkQv49JUNwkIIgRoEUin4MBFHikEhdoEpDURnKNgdL+WCp6wIbLSE56kZzeFCgFTR1xiwQkrqaM3YeWWbHXmUiIWUdCklQLec4ZxgHwThld4cQLK9fv+bm9hwhE2ePPE39AJEyGYGUa4SUCqkFuig5PZ2zmDuWi8ccHjxl2+2IeLy3FLpl3hzSVgu00ESRIAW0DPkQdu8FlzKbbL/yliLjCmXZIGW4x2V/b01BCHWvLr57y/YTdwE7f39dlG+X/WjeD7m/t6LYX9h5j51BSqHkPucgryresYrujPfEXp6e1cp3+6o76+27ppD2QDhCYbRBxJjzAYInRUvyE8nnpkDw+Q+UBCIGkrMIqcgpjjInWaWUC0PwWc0YHNMk8TFQVi11vaDRBmNKgk9cXb9g27vcG4Ukeo/Rgsdnh5yclJSlwLqe29trVqsNISrevl0xWstscUpRZfZVO1/y+vyKvk+cLRcY3RJEDWEi+cDO7lClIsrIb379cy7fvKHUhnnT4GxPYSJlVdJUmlnbMo6evu8J0TOOE5Ec0elsJETwNhJsInjwjv3FBkOCWgpcUFzf7thuOrQuQYAuIriA1HmaOzxYsus2pBCRJk9bpLwuMgpkmfnJKQkQGmlELgJjQHiF0FkoaIpI3RaMPhDsluh7lDygLg2jiwzDRG32fHCpKZsZR0entMsDfBK5yTmw48jx8SFi2nHZd3z49EMePzrmV29fIn1PfXRAJQJPDkqGXU9tEhUDjZREbxFAUSgCKU9UyeNjycHhA1a317hg0UbjMCRhePD0U46ffsbf/uLvMEUB0TNttwQUr3/7S5J1mNkRs8NHSBkxhaCWER9geXKMCIGgNC6pPDFR7Fly7zJD5ssDDo5OKIuK5CA4y9Rv+O2vv4Ru4LCdUTYt+mZHt7rg9OgRB1ULtkYJn7tqyhZuLqR3GiQpkDpvAUKK2S5CBoj5Y5Lcm12arBZWGo+jswNYR0XIbLsIZSEwUiBjnkhIYGOeCrwo6C3sgiAIwdZaiipR1YYpBqpKEV0geoubFIQK5T1aRGSIGJ/QRt5ni3tgCntDOimxPjF6zzgmnIgk6yinDct4QNtWCAOCAi2hbQ3zZgmpJyafzQGTJApN3S44323BO2RR4XygjIFCS2Rh0KbCaZ3JKpuBru+YzUtevbnh66+/4ui44uTBkpg0hZTEpNGmRsmSpMkRwFFSVjMKI6lrOFh+wJu3b+j6DbqQjNPE6ckZSpV7G5zMyNIip1aGIDDKoLVChOzxBAIfPE3dUpYNJmS25u+5KeQu/F17igxqcK8fYF+Q4z5XWO5D3MOdO+mdcZ3I65mYIlGL/USQdQX5e/I+On+veK/fpP3a6S7HYU+B3a+Q7vIegP1uXGF0AcETpcTZkWAnRAqQ/P7YEvIBCIUWEvYTjdyvxZTcW2QQSSqgiCQhiHvxRWEKirompRyUc7O65fL6NienaYlIEe8sRzPNhx+eMGtB0aGkYbfZ4ZzAOolEo0xFkhXrbUeK8LuvXpGo+IMf/yFf//oXTNOEEobdrkPobAmtdcLtNvwv//7f4UbHwXzGNK65ve6oqxwUJIRk1w3stsOepub2O8mUxWY+EnxeJaS4152Efc/cp6nqoqKoGpydcDZgFBRGonS2mC4rnbMqCARACc2D4wcIKbm4vGT0FrUXOmb9iiAJyWK5pF0u+N2z15hCczY/ptutqJqE0hmDOlqULGrBIHz22I+SQmm0kKxXA7urNZVOSF0T0fgksSESoqLQhnG7wnYrfvijHzF2NwihefjkKVfPv+LL33zJDz/9mNfPn6FcT2lARI8SKpMgZMaTiqrCSsHQO2Rp+PRHP2B1dc7XX31J7yzCaFCSr7/5hmevXjGMHdoIlnWTmUrKME5rfvc3/4YkS7ysefTkc5r5CQ8ff8hsfkhVzxktqGpGkSIu5PWs0ZkwkKSAKBiGHTEJTk5KCq1wQ0+3ueD26jUHMiK8ggB4y3b9llomChHw5DWY2SvZg5+IIk8hSSmElATEvTW8jwlcRAZBdJKUBErmbPWUZE5w6zvGYaCSe32AToiYrSTsaEneUSixJ3FIhDJYr9hMHmtqUJLVasN4NVG1msMDwcFCkXxO6RNJooVEBIEWCeEsUkWKUlKWOdhGyRKvct0Yp5Grdc9mAp8kPkmi2KIpoR4zI63QhGAQUlMbm4OmgCg1k5uwQbA8eUK1fIC4WmNjx3zxCF1NTGOHVommrlC6oG4qxjGx6yzIEang8eNDLi8NhUkoOULqkHKOUUvKogWqTLpJOa1xHCeELNGqRhlFXc3yCl4ntC6p6xY7eaIICJFXRFLn+metIyZPjHpv65Mtc0hQ1RVVVRN8uq+Lv7em8N1sg0wjzVgA5FVLFqJl+l8usu81Fbm3rLjzMdo3lRQCQeRRNsbsMySkzNbSYW9hrWQWne2N7VJ6R4XNILP89oyS3sWDQp5EYoh47xjHAWdHJNl2Fglh/6yJEa0kShuQIv8MUpCBgogU+39lJIoISKSW+TZC4GNishPDMGTgXQExp7sZLTk7O6CdaZoq0dZzjpZz3OToh8hkHT4YlC7Y9B6ZauqqYXKCpjVcry9Y7S4xhWDYjjRVw3aKlOWM3qbMZEiBuoRZCwcH2cGzKEqMFvTdhBA+G+/FQLAuA+kIvMun1LvdcgwQfdwH5pBFQQG6wUEaUUBlCkKMTNaDyAwN60eMUWx2A0pKiqqg68ccZBRAREkKAQggE1JBRONcYhgzS2u+OODw4AHPxx1GhWzVjaLUAT9ukeUhMkkIoLRBpMiwC6xuB54+PsGYOd4LotIkFCElHp6e8uzmgi+//BI3PWC9uuDRgyP8HsBrkLx49pKhn3B7mmyMMq8i9kp9QX592snjXUQYz+R6IhYXRoSMIHMUaFktWO02aBVpK8V8ZlgcLHjz5oJZ1aB8ou939Nsbvnh7jihqLp98wJOnTyjLAlXNkdUBwrQ5MEUWlNUskx9cQGpD3bT048Tt5WtuXz1nuHrJ+YtfElavaCtLFwTrjWXsB7abC1y3JrktSkRCApkEMYELgoACDNFDUdVEH+j7bO2CzNepTqBSnvqcDyTnSCLjeuO2R/iIKVO2VEj7i3B/nQvIB5OUG1KKktvtyNU2MeqOKPYiUlNyO3iGMND57HIQ7ETyGdxuyxIlM/srxogaIkURKE1FXQlikjjn6PqJMSaaWmFMwXwxp24rpPIgR0TyFCh0Mqg9zgYJpMaLxBghCI2ulwTZUrZn3Fx+w6uXKw5ONGUlMfMCJRWTHVAmr4Yvr54zuZEQS4pC8uHTE/qhoyxkPri5EaXzIcx7i787KBMZJ4syFYcHVW6GwWLdwDRMzGYzUoo5SEftnVj3a9rMyLQ4F7BO7qn+eTtRVtXediO7MMT7C/r/99s/Qqcg7wvknSbgzo4is1lygb4LyhEqj6DsV0xa62zIdK88zvcnlSTEPNbc2VN818PICINkn+EA++bwruXcgd17uPn+ud3lLwiRAdAUAm7KRco7h4j+Hp2PMT9eWSiWyyXDNDLZKZ9sRW50kZCdNVPI2b0iIIkENxGLCmMaxtEhkmAxm2GnEVIgJcmi1RweNaQ0EFP2Q/fO8cHTjyjrHW8v1hTtkj/7iz/n9cVb/u5vf83BwRmnjx7wx3/+U4buii9/9ZcsljXGz9muOsYhomc1bWG4Ot9QFpIHp3PaOsvlq7Jl3i6IMdJ3Nls7xAk/WkTMFtORvBG7bwj5EIJI+8hNKYjkE+M0BqauozQKtWj46Z/8lB/84BP+5//5X3J1c0FVGcbJcmfQpZxnGrdoKVFCEb3NbC5FBuuFpCxqus6y6SfqasFyecJm26OUJkVHU1c4H7l6+5pNr5l9WDA/+RAlNArNvG348On3CN2W2eyQo8OHjAGczS81rSRNU/Onf/onfPPFf+Rv//YX1LXin/+Lf0WaeppkmYKlvzdeUUgvqJJkGD2FFoSYUCoxuQllCo4OWm5Gy2+/+I/4acS5gCwgJYsxinHaok2i1pJSOppaMtgNdavBThgC80LjxcR66DB46F6xeXOdleRe4uWMsj3m8MEHVM0Rsjmk0BUKQXIapUD5wG694+/++l/C8BYdtyi74vyy5zoJUgGxNQzDhqglqdtgvCOkrC2BzPaKSNTesDDFQEwJ28eM82SeCEZAqbMq34WA7btsqe0cOu5V8CHrCyKZnumiAyFQezA7bxD2uQ4kVKkwZU0SeQ01TJ6Ew6uCMSh03j1iQ8QIiUgGI0QmfgiXMQUSzsM45hyUaRwIIWJKyaIuaeqaxbxgNi+w40BiRBeJoi5QWuC8g6RJURAiWGcZksBrg0saQUVRH+H8Ob/+1UseP2l5/GROZUbGKbOnUDtcCEhtKVTAx55oA+28JKaBFLNh5Op2xeH8DEHAByiqApkSZV0gpGQYPH2/odt03K7OQUTqtqFpqnyQQuS1ecgbGB8iPnpS8nubIUDkLUxV1iwWBwBYO2WbmN83ppDPlPt9Pe/wAPbBDXmnn08GJPYri32RJpGdCe/WSu/dHLG3vNgrn5XiLqLzjkGUldQSmeK9biEEl3fV5JMtSiFktuC4t+EW2eMohbwaGYeRabB465lGC8FhdHbgTDE/V21AmYTyMY9pIj+vFBzBW1JwGXeIGUyLPiBTQjRzzN7dVWhJUWUvfG97FIK2VSjpSTHgJ8eIwg89R0dn9NYjq5Z6eUw3wbYLuKApygXHx2e4yfN//b/839ExAoHZ8gHar5G2o9uOdMMthUycLFsqo7C2pygEuhTYMGJHCyLgpgnvLdEnYtgLAIVAiiweSxHu5CZCk/UGSGIqcCHhowUSTQGlTnSbK375i1v6bkfb1Ex2JOzvI8X9ysUUCCFx3uVCsLc7CRkAwvkR5xJHjz7i5IPPqOZHfPWrXyH6LNpLwaOkwRQzeuc4KvQ9B1tH2PUTt5uOVTfwUf0UXVTEbsxiw+BYzmdoHL/425/liFAJbaHRJPpxYIieJAIuZRaWAkSKFBqCAC9j/v1IMKXhYHGA0gW9v2W13WTdjAGhJdIYpEgYYREpYaSmaWaEmJhCDnUZpgFZFCilKSqH6T2NUhTBIvqBoszU5tFvub14ybi7YH5wymx+yMnhA6p6hqCB0aGoWM4qPv3e93j9uxETC26ubiiQIHM+h5aGqe8IQtDvBgopMhMlSbJpZiLEBASCT3TjDqME0SVEzEIwBCgp0CaXi8m5PDGHrFGZVTqHHQ0Te/ZvvuQDTClk/yCRRYqRzGw6WRoqSjpKHAKKmt4nutEjlKIoCsI07n2EIs75bEkuNKosKOoGqbKATaSAcxN+fxIopaQ0EiUdVVkg5ECKnqoWmKLNan+y36iPFu8d0XkiChciNkiqumLRzEhCMK9KWlVy8/qaabXm9nzO8ZNjPvj4EbODiuAcUktOHjxmtCPODYAnTVtMWRGjIUyaFPPvTxmBKcv8ekmKsiwZhpEYEoJAiB3juCYBh4cHyJRgv4qWewxWkE02JTqvb2G/QspMpBR93lLEDMYroSiL37chXtqvcu739eyLdpal322RslYhg8vyPa1CdgqN93gAdypoIZCoe+2BEJm5lBsQ98h6frRMU7U2F6eiyIKz/HC5AWTH0zwmxZhQUuGcY5omhm7A20DyMA2OFByyzspA2JvWqUQ3bAg+kAlMYX9i2mcj7CmpwjuSD0gDhZyhIO/anUMbQ1EaCi3o1p6mUhwtC6TsIU5ZjyArAL55/gyrZojyiN5HfvXF14z9QFE26LLgZ//hb+j+9S0khU8lt8OOg48e8JNPfsq/+Of/E9OwJYwTx8eaphRURoNq8okruhw3uXefzFNSwnuBUCrztmNChAgRZD4YYqTCSImKPquxhaJ32Wiu0NCYiJEjN1cv96ulAFFiHYSQWWchZdbSGCZKrdDsDwMhh/QoIXAhIsXE6YMz/vif/jNOn/6Q1W7k9evX9P05SkfGYcR6jzATH/zgh8wWM4bgUbrK0ZIxIosCURQ0ywWjy+aHIjgqCaUcub645cvf/JzCBGw3sGwXfPzBQ24uDbv1CjtsCUFQaUWwjpAE4xRQhSTJvDdPEcpSMA0WFwdwDhUS1kMyYIoi78qHYX8BK/AQY8XE3TlJIkyZ6c5S44i4vf2IChLjM2RqYoKxZ16VfPhokRO4xmv8buDN6wEXCpYPPkI2x3g94+nHP+Dk8JQijvx1H1i9+Qp0JAi/F5EIVNWg65Z+dNjJoxCYlKNVw509PQnpA2WEUkChuT/5F6WiqA3Oe8IUkXs/fyXBqKxVUNphpwxAZfK4RqpMDHHBgshEA0KiEpI2ClbDyGYKeByV3GsCgsQNI2O/pS01xwcNpRJI5RjttH+NtShTIpIjugFrXQbwE7SFQcmElAGlI8oIUB6MYgqJaUqMkyfJjGklIkEkQvBEoSAm/DCxubygmR1xVDe0Ai42E9tt4u3rHcOvzvnTv5D82T/9CVUjkTozo6pGQAwM45a+f5nZP8Ups+YxB4czYmhRRmFKQ0gSP2a7fGczSJ4IFKWgKCUkRak0koQkoWXOLc+kfQEYpBZImYgp4PcHWSUFwTu6bkthakBlYW14ZxT6e2kK34rP5H4LtMcI3ukUxHuahTsV8t2bFPs/QrrLO5DfXjm9Z2vx/tu7r93pEOL9fefEMoOSev+cYj757/ea3k2MY4edeuLeIM97hzEGoUVG7GU2z1LGoLTOLAulUFISnSeECUIO5BEp3dtdRB/QOnv3TMOWalbQtiV1UyGVYHVzycnJIY/OjnDjLSKOyLC30xjHrAROBaKuqHR2GE1R7KlqkuevnnN7e83Rcsl//7/93/H21Qt+/h/+ijevr/j6i2e8eXVNVUSePpkzn0GII01bszw4YLNbgcjrN+tyeE9IkkjE+qzGLaRAxAQhFwFZiCxSjAEVI4aEMQrrI4qE0VAWUBQSrQRCJbTRhCkw+hxUxB34L0GE7EMTfUTtVxBG5QOCUIKmMBwfHdLZyL/9l/+crf9/MXjotitiv2ZbCpL3lE2FZc1yu6PyPiuK97Yaymja5YLD0xNMWbLrd/i9FqLQEj9e883XX3J79U0ODEojq9UF5tOHfPjJU2J8zGq1oq0K+qtLNtdX4G3eW5P31GqvmZmsw4ZtzqMwCrXHzSJgnWecPCEGtFYYrYFE13WUVUmSkRhyVKRWGjtZQoq4kOj6iXmjECGiWo1MWYj18MOnnBwu0YXGacnlxTmb6x3dkHjz+pz66DEnH3xKtAOV0dkWnoAVEW0ERaExpWawI4VKFG3J5PLvz4aAFCb76MSAD5kYoozI0yMRY/KKV8REkpLJWazz+JC1DoKczDZ5iwsZo3J+vwHwCU1EKIXfY0h5jazQWjNZqBQsSnDTRD9NzNs5RS253ayZrOOoKjk9blm0DVpEQvJMznOzHnD9kK1kgsdOAyJAW9foGJnP2gzEC4kLCZzHRYuc9hRzFNZF7v6AiazZsHY/NXlJP674ZjsiZYVUhquLGxpdUBRFLvresV5HUjikKpb/X9r+69myLL/vAz/LbnPMdenKdrVvgABIUByBERxJD/M2Mf/jxDzMgx6lF4kyQ3E0IIYCCUAAgTbVXVVdNs21x2yz7Dz89snMapIxjWDrRGTkvZnXnXv2Xmv9vhZUQDuRpL568ZyPP37OOGQeXX2HHNbozZZV/whjznB+yzhFcsooXQlxkKiRthH+RjWcrdcoZcXvo6UBEmRiAkXTdPglUqXUREkJpTKVsryWsN8HujbS92cYa15L/v//Pf5+Hc1vSUpPWLxSy52hqvx5+8TPaTORf9fm2x0ItcqLqsybjeA/tim8vTE03r+GjtTrffPNz7R81qILDhgDXethMgszUWhbD0V09bXm19HfKSfKa1lrFf4hiYuZUjBvmfGsUahamIYjqSh829M0PakWXr58ASXy+P0PcE5RojiGValUpZYu6Ern1jx79IhjkhrOqh05RpSpjOPA7rDneBj49NMv+ZN//I/5+tMv+F//5f/IfDyw6eHJowZnZ2rJeKtI4UCOVsjlMZByJsYk0lK13MkqinQvS2GMBZwWL4VbHK6qyNs1idVYU1hMzJQiMdZlTjjlSEhUiRyylhOkUovBqaLKIixAFlrrvJSW58zty1uGkJiw6P4cFTMmz6Q587CXjaiqRHHw6aefMDZXfPiDf0hZZJHGWpqm4f33P2AOE2E6suparIVVr5j2O55edeQfvcv+9hVkQy2FYbij3zxBK895+5hu6esNs2d4CBRt8G2PLgJ6UGdRS50c/AtG4h0EZFMoKKwxb64/JZi1jqLISTmhMHjToI2i7Vq0OZITPOxHgoOm9cQU2F5uuNysON69wjhDmGYON9fkMXHeX7G5fMTVe++ju46H6cjd3Z4XX37ObrjHr1uUy/RnHattR727JaeE0uJAN2aBdY2laJnYnZOk2BpmMpC1Ji3iEQxkijhkjcY7jUIk48Y7ahazYalyDcQg621eyE1LxTolMSPym6HEgNLQGcPGg6+wOfPgHGszMUfYrDvaVqMYSHOgUEhz4WKzYUqGF9d3+KZBKam5NUbResilULVBaUtW5vV1W0/XYS2kHNFVU5eDE0guWS2gi8Fm+TpTPBJiobEdSYlc/nvf+wF/8P53OH/0GDhnHBpcY1DMKDUJxINmPCZexSPHjaLpFU3TkIuhUJmmzP3uDucAnfCLYEXWlY6z841A7kagvVpFETZNE8Y4mqYBlUlZGiFDGKmU19yu1hZllUSMx5muW0ny9G/x+HvVcZ4ep8VZLe1qZQm1q7UsZLCQvt9yGKs3PQei6Did9EX7/B/7Xm8/TnJWoySe+zRtCO9wmmTgtAlBlUgGY8lKMyCnfectOudFPrtgXzVLGB2A0rIDl0JJacHGTmorOVUorXHGoI1EUtQ8s79/SbWeOUVyDDx5/AilEvvdDqsiJcyolLBoUpKQLz0MHO7uGJKhqBbrempMJMB3PZdXjznc7/lf/ud/wasvvuSTX/yMGiYuzyybDroGVr3BO0Pfe47jwP7hFmUN8zSTM6CcTEHaU4pkOoWQUanSKmThLQIhGSe8DBSss1RdmGJZggsBJYGHuUoMR1UFzBJjXhTkirHLNbLIF08clNJmMROCipkwj+hccUpTdSaVA4SAzgVdpb5UO8McKquVZ4gzd7f3fAi0bUusFpTm8uqKowlcP/+Us96hiShdZGGKO7om897TNerSkuPMcX9AMVDLSECB8Vzv7oFAu22Yp5HjPrHpoG83DPsdoKmpYr08n5TS4l9wpCQMvTGi5Y+T+Gy89yhVCWHEGwe1kkskl0jf9/TNmpsXD4ScOAyV5KA7JrSp1BS5v3kp/gQF93f3og4yDeebllXveHj5BYfwGTdT4Jvra8LxgLMF75x4Uy42oDO2MdjGoHMh2YrzRhJNU4Sqsc5xfnFO6x2HuxvyPAuBqxW15AXGZUkjkKa+UhTWaOac8M6+vvdjYTmBi+xYqyq04yLwEWNVxWpFqRFVCitbaahsTBA5awf6rMM4TS4zIUUJqJsCZIWh52xzzu3tnvvbHReXW4xWxDyjqByngDIaS8MwFxqFwC410TgvB8kKqmRKlelOobCLBLmWgi6yHqiiMUpTVQZtsNZz+egp26ffAeuZZyM8SZpoVzMx3aLdyAffuSClgeFwROlzUvI87APzHNFqZJomjsMD27MO72UTm+cZaxxaO5rGL9ltAapmSrMkBuQsHF2cmeeJGCM5i1DHupOJTZNSwvt2MbCV1wfw3+bx9yrZgTcneaEFTouvmCVeP+qb/A2l3iiVlv9cHMqLS9kYgWt4oxj6zanhtPGUJUyP08bzlpPv9YrF28qkxSdRZHRPKVCqdACM84hVIkG1zqKQXdUYSyVLmqrSpKIkaVVDzRLJcMLf5akKxBJLZP/wEqyjW6949OycrjVoZp48veD25VfihI4RpSGGjNGVh/GeVzf/jra/oFtf4to1yjYMsZK0xZmW8zPN9Ve/5i//7Z+yaTQ/+O5TVJ2wKmB0ovWO7bal5IjTmpASaY5Y41HGAg6tWlbrCzabS8Yp87AfON69oo47EhFMlkyfuhCUVnFMMwZDqYFT6q3WYL3Ct+A7h7KGHKQpq6qKdpJ/VJKcyozWFKUlx8Y0KFMYQsCEgEG6uVPJomquBW8rSYmRLmQgKnzjOM4BvWpovcFbg3MKsGhtsUpxf3PqmE7kNKJV4vk3NxAP2BJwNeN0JNeRwECdRm6eD/jNGZuLR5jeMEUoWrN9tOFmvOPV7YHQJRotipeQJ6gapzWl5gUn0VgDXivySdpYyiK6yDizhP4tXBNakdIsypoCvm2Zx4NsKKWyO0aurloKlf3DLWMYCTG/1pk/e3qF0opffPxTxpDotucEDJ3LrM87+tZzdr5iDgO5BuZpQCvovKFMic4b4rSQ/tZivEcZQygFYkD7JTgyZ4oST4RShVKruKC9RVG4ONvgjObli1dUZ+Waq5luteJss2V/2BHnAe2sLEaq0gh2SJyL3GdR+DlLRVeo0x6SxtfMdtUSSiEvh7FSZKPvmo45zeiSaNoWMwaO40hRFqdFH3h+vhVASBtyHslLdE7NFWeXQ109tUcu/ObijRI0QO5NZwDbcAiJuc5Y26Eai7GelAy5ZEK+w4SZxA083KPtgc3G0G413/1xz4tvHpinieMUyTWh6EhFE2LBaLcQ8gIpT/O0NDt2IspRAkcZDeMYMVp64YcxMIWM847WS/3vyUislMFoQ9GKFDPOyYE5Z4nR/20ef0/z2pvF+836f1r8ZcGX999ok2UqOEFNp895SzL6FlRz+nqnv9/uYT5tDpL1kV7Hbrw2w+kTj8ACCcmGpar4E6TbYemAdo45zBRdsabBGy3wUa7kmtAY/GKco0hWU8mKWCRxVU7F9fXpOecoEdJUttuVdP86RUkTmIaSgrzIRfoUUsrkbEQFtJxYhoc7whzpNpFmdYa1G6ryaDJhPrLpNWd9hyfQmIRVGqsbwNN3K2IYiTGRoiR15gLWSx9trYaIhuJZ9xdoZ2jPLPHyGQ/XX/Hw6htyTRRjCXHmD37yh1yen/E//fP/kc5lvNJUMtpC0yqaztL0FmWkkyIvUSK5LC9urpAkkpkCWnuK1hS1RJRT0MTlLagWifhVcio2VhJ2i6pMWXTdNQQ2W82q7/BOYb30auRasE5OVoNRktmjAiUeefXiC2ocKfPIR+88QRGpOdAYOVzkGIn7wERifbbFrB27uyO+1XSrhptvJkwdoPVowV1QRiIeShWiXGkxNhitpWOhZLQxGCv8jVZ1MVnWBfeVeyGEkZoTq7W4qGsSGfc0Z1CecZ6AwBQiIULfaYxvKdZxexwYYyJROY572u0FOWsoWTaFzYpXt0dRQAkTjFtcmLFkioT9UnPBGkcBxnliSoHWQIoz3jlylZBDbaQCt/WOVdeKwkVrUgXbdUylkJRBNRbtHKpxxEMmWdBGYOOUYLvZYjLcXt9JGF/bczxOgIg6Ss4oMlRF3zWkYSTmhDZgrKZbGUKq9OueYQ6kkrh8fMnuuGOYJjbrhqtnT1ivWva7gXGaoWpMzkBCF4gxiUTaKMzCGwEitZ0T5IRRArEVVUFXNq7FVMdYDbZt6dcbitYYmxnCLTXvSNxwGF+g1EAqG1a9p+s8Z48st9dySLbLtJ6LpS09IUAME2KiLUCSHggtG6FEZszMYVmnfIPWUi96PE5stMWtWpyVySAthjWZ4vyStyb+KqUy1v6HEZj/pE3h7QKcN53JIjkVs1h5Sy206Pu/xUMscse3NoTfdEi//X5K6VvFOyfXcl2ykF6//+YroJVZvn4Wxr0mCe5awpqqPAG2mzWqBJwGakRl0ElOREobahTsU1VJgtRGSwGMYjlVFNCGdNqgDKxWDdu1x5iEqkIkj0NgGgfCeMSmjEaRItRiqClKcmqCzdZideW4u6Fqjek6tHaUMlHjgd4HVDqiTMAbTwozj9/7kM36nC+++JKcspQGFdA0QCUMkRADEs+bsHTM24DpthTTsr1asT274PziCV9++TmHeWRz8YiHYHjxyVfotuc47KiqoA00HTS9QVnIqqAR5+TrDQGggNOapjM4bQghkYri8ZN3eP+7P+Cb58/57JOfs3KCu8aaqSfXcBX3tGwMGmWrOHprgSyk7fOvv+Di8Xv86I+eMRXDHIU3qjktCo2CyhM57dj0luNe8/U3O/Jw5KJXbFqHU1BKwmtNiiPjXSCNd6wvVzStEP1db+l6aLw0AKaycGLGEuKEdoL3ZiTao5aMUZrLszW3D3fUXPDeLtk0IpeOMZGqRKpgFEZbVuuWYdXycDvIBqoUKSv6zUp8Eo2l7Q1Ke3ZjZrjZM2dDxvLq9pbNtqfdGvrOM41HwjRz8/Kaw/EeYwrOKlZtg6cSwoxKEYsU5Igj30pApU5UNZFLomksbd+hrWU+7nBecHnrDa61hCkzThPjHChoplQxvkOjGGNiuHkpIgdjuJ8TToPTjlAMw2FHNYqqRZ8fYiItk1XOCOzjLbvDwPmjR1w/PMihzFhyiGjElHWYAiFOtI3jnfeeoU1hvWo4P1tzd3fLEAKN74S7yxX1Wqoph0XB3qvIyankGrFGpiGntRzYgG6z4dHFE47Z8vnLBx698y6ubRlK5HC8I5Qbmn4k5AMvXr7g5YsbnjzZ8tGH73NxZVE0tP0a0VOCdQ2mWFJcIvmNqCZDiDSNxlqDNokQB+b5yDRNVBx961BUrGtYrTRGe3zjqAXGIS498hXv7SK2fQPX55KIyRPj/Fut9X+vkp23T+tvP8RolhansVtI4G+TvifI51swEwsEtZTLvx2F/fbXfvvfjTGiG8+ZFOLrTUMvv3RqoeZCSWJmkZRWRVEKpQ2+aXEkGpNQaYI0iYkN8BpMEdK0pknImoVUVErjrJR/p1gp2rx1Wqys1g2bTYt3IhED6W6NMTOHQAmZErNMDFmRk6IuHGZjwSIJrgqDtfAn//kf8/GvPuf5l19D2NN0EU/EEsgxQql4Z2nalophCll4AirOGnIuzIPI9+T1iRzyDddNy9U7nm7bC/Gk4PzRM7rtBRX4/ve/T5iPfPXpL4nTyDdf7Cml0jaw3jS4XstGu7jQhMBDTMon+XFBxm8lkQZdv+HHv/cH/OAP/pj4b/+cX3/xGYdph1MV3QIarFqMc8ulUhaMrizTpjaOEgv3N6/45S/+lu2jx1w8+xDvHeMhsD3bEPeeMD5Q4x2GA6rOGALvPutoraaWxBwLIQeMVnirJP4kRUKNHI95OY0VnK/0vUBaKCEkm77nME0cR3jvgxXhMFAXHb02hs1mxeWjRxyOe8ZpwizQqrNGnscyG9VSSGXZUHJH00jrfEFhtWWaE2d6w3ZzSc6BOQb2U6XbPsWtH/Pqy5doY+jOn+I6R9Ed637NcJzZPRzQKmKbgm8tTldabynTTAoRby2NLaJ0UmaJSIcco8RoFzCm8s57T3Btx5efjXgD1LoEIhpmqkh/qyKjqLajupYCzPPDEu8iSsNiLYep4q2lKw7dbhnDPdYaphhQRgm5XgphlshUb1p2Y+Jxt2G+uUdT2WzPGV/eMA4z43zH/f1MCJGtO+PqcoNvHK+uX0At7PYD8xR4/O5jHu4Gag4ic1cVq6WNRS8CFRRUo5YYG5lsc1mc/himAs+unrK/HdhNt5j9RDdOPN/fcbv/mvVF4smqY/dw4Pp6IIaG42HDyxcOZy/p+g5vzrGmpfFy2h/HzDQH2Qi0eAq0iijdUGpAlURMO4b5nnEaUKpFa4lr0Vru+abtUJTFZ2SWgD1Bc8bxSEzhdWyQ9w7rZHL9bR5/j03hzZn8zUJdMUZIWaWsgAGKhdjQ35KpnqClN4TwibxdxgfeTCOnjzkt+P8eyb3ARieOQUp77LfY9bYV/M16j1KFPA8YInk+MKsIusNkiGMiDCO6lsV1LU81V4mOrCc8XC/GOk5QAKRYwDh5kTonBLYqUJcNUllROGSJSUixQhXwZBoDFnh6ccamd+x39wzTSPUNtQT6pnK8/5Lh/isaM2O8RiNdynGWkpP7+xte3twyp0RGwrZqEdNWyQlywVRFraJuUHni4dVXxBh5/P5HbM8uGKaJs/NzutWKiiKkzDhMnJ9tuXNa8HQNzUrjOosyi5kB+X1oY1HExVBjMVrjaqEEkfLWosBVmtUW9+xdTLsionDeiiFQy+mwLDepM5oSJIht4StRNVOCNNmtu0xj4dWLr+jOzmn6M4FolDSiHR9uKfMrarxDA41SosAplb7d8uzxI169fEEKI1UjMQpVeJ4wz3hj5ZTvNE1rmA4Z5xXaWKwzjA+Z/RG69YZA5XA8Mg6JpnPc399zPB4I40znLVoJT5DGCaUdRi8qPk4mzkwh0nV+iSIpqKZwPM7sHgxXl2vBmodCqg6v1sS6RjWFD77zXWqd+eSTX7CxG5rNI9T9num4xxJpGit5PhratiHkpS1RaVQuNNYQquKwO9C0jvNNJ1WqudB0llwDt9cPoAvKVGlsy5UYRkKY0MYsnMiKs7PHfPi9H3I8HPj0b/+amCa22y33+4NsfK0lRMXNXrGyHttsGeOBVGYapTDGklBkVUhFk6IE5/38489IJaGdIsRMLRpVRKQR50CcEseHO9Q7V2g00+GAygWVFU5Znlw94vHlmo9/9ldUEtYYUhASulS9lIAp4R+WRN9UK6kqqvZk5VF6xfPbiVf7hGrO+fr6wG34mGNKHKYbNoNhDh03DweOe4VxG1LeMI4bNO/TN4+wxmB1h3M9KVSmYZagQ2vElV0j1leUTYzzjjLvUXrG+glbJkKYGWZDrR7vPNY1WOspNQthvriZ5fCkWG9aSknMYaSUyGrV03aNxGT8rjcFGUfKtyYFIY1PsJDkE5X6hjfQ+s2Cf1rIT/AOLPDTf2RCOC3yvxl/Qa045zhFYczzvBDQy0ZiNG3b4hsPxgpBtuppbcWWmQedmHeROs8UBd6IwsBpidRVy85bKKSKpEIaQ0WjlsaqVBYFTtPgOynLKFSMkhG0LrEZVNF9p6XDN5fKPAfmKXG2arh69IirTctwuMdbCLVw/eob/tv/5v/J5dmG7354wby/I497Oq9Y+ZZsE7bxDOOBMSXGOZOioyaZkFjiBrwBitTxdY1Ib6kDYf+CtjzGRMOrL78gh3e5ePIuY4gchwMmzwy339CpzFkrOVS+d2AhV+ndruKqWBQREpVhlHRoq8XPQS6UBOP+yL/51/8b/+7jX/Pli2+YF2OgNqCcAmOoSVy2DktWEV0loMwZ8Auh67qOH/3oh/SXV+yHB+5vX3KuxUVuvGc8Hrm/uaG1RywjJWds1VxcPMG4Rjok1hvcNDPvjXR2e021lpBmmiobkFaarrOUlSWNmUpGGcM0T5xddHTbzHGaGcKMcoYallrSoggh0HhH2zhSihijiSlLEYyCqipKg/UWax1ZW5p+wzxWxuGaWipGWR7uDgy7A0ZXitbo/pztxXv86B/9l7x7e+Anf/gHzMOOz7+6Jqoet7pge/mUedzhlULpsFQ9zsxRpo1YRFasCgtWB+9/8B4xB7ye6KxlHkQ0cP9wwxgzq0ZJonCViPl9CkxTQusWY1uevfch//BP/gv+6m9+xjxmEo5C5mFIZNXxwUffY54qv/r5r3n6ne/wnQ+e8qu/+ddgIs5EdIocp4lQNRlNVY4pVS4ePWEOIzmJ3DLHRApaDgcp0mjNtjWoEjnc3zAeDA6YdkdSrHRtwye/+pjtxuOtlayznDFKhABKQ9t0tK2jknjYH7DWYZ0lREOkgew4Rs/99YBbX7G59Gxsy26c2LZiQDscH/h0dy99I6pnv59IYcfF2YfAGSVf0LTSDhenyjCMhJDpux60IcSRGAcwkXgMjMM1hTucy0g6t0xcc9Q4t6V3l1inMAZJSY0ivddGHDNKQdeJt0IdZjFM6sD9wx2Hw/53uyn85oL9Bkp6U4bzJp0U3p4q4BRlfYKgTlCSbArl9b8LPPSb3MLbP8Nrl7QS1l5pUQ+d0gGN1W82o3qCthRGGfrVlsdP36VzmmPrONwqcpgxTUHXJUJbiYyybRtylXpOtCdXSXoVJKwuI7KlbbulpH4mp4RTEhpWUYKV1lNf6jI9oBnHiVrBO8f97R3H24B3Hgm9Kjx78pRHj8/oG8Xh4ZZiMnhxNuasMMZTq2KaZ2ItTGMmzktr3ZI5b92SWlorMYijuWbwviWnia8++Sm5wDAlwniEkjlMMykl1q3BxIEwPtB6JCemFZihZr2ggIpxjIQpMY2VHMCbskQnZJySxrlcZCy/ub5mvHlgrlkcl1XUSRrZdFESV6KKWPL18n6tiHNcG/qmY73qQSnaxnBz8wLjWzarc1TJHA4HDvs9bh1RSkZzTWUaD9R5Jh+OvLq9p1SN9T0WOB4eSLFgdUNOo+TKKCEindP4RvTt2irmNONNS9931FpoGzE6rtc98xzZH48AxBAlTApwjRjAaixoo8UlbziNQKzXKy4vnrLqz7i/2zMfAwqFSprDbsRYKRMyNWFVQ4mZ73zwHebjxM9+9nPGKfHB+RPWF89IKfLN15+hlaHEgnENqUTmEEgpU1DkkBZCt9I4yz/8R3/MbnfLr37+V8SaIMu1XWOk8Q05TYLrLfdcKhVlPTFrXLNiipW/+9nP+eLXnxLGiTweJXKiQMgKq19yOCRqhcePn/GT3/9DvvnsF9y8uIeS8DkzzoAH4xxFOazr6DeXXHYNv/70F5KC6ixTiOSYyamw7nrOzhwhB4aHex49ecT28SPu7w7s45GaEtNx5MnlE2rfCYmcItYa2RRqxVpN0zhy0bRtg3Ytc9TiqK49UTfE2jEmzd3tkdVFT9t0XPRrlDFsueSqBOY0cJhu2A2vCPFaoJqaCTExDrPwFkUSe1OUtanxDnRhmgshBslL4kgIe7Q6Mk5HtM64xqNUTy0tuc44r3FChKK0xltNjpmYZnKKwv1VmMaR+4eX5DpTiRyP0kf/2zz+Hj6FjDFvJoTTIl4rgr2/RT5XXV9DQaKPlc/V2rz2KLzdr2DUMk7XhXlQoE/SvlqWCGsFtZDqSeKqUXaBkZDVpSAncecspULKElzn22YZUT1mdcnWtazOL/Hrc2Yc0+4aXSKEic5upEbSexRF/HhKMccgmvEsJRpaQ+db0eTnhNUVpwyqZOIcRM4aIVLJGOacmEJk1bb0ncZSWbWgaqLrO7ZnW755dc2T7Zp333sfrRMhPEAJVJVwrSyexjeSMbOPxFhJGcJBFtNSKq6BrtHCbRj5927VEkNZ0lALcUqUuEwUaMrNxFe3r5hSohrFvO05P2/QNtBuLedna+Y4M4aZohWlOiqWcZ4ZD5npUMkTdG4ph9dVlBtKkaoiRIn/0M7SO0OuEvUrefIKVeRCLyUT50go0oIn06acluaUeDgeefHqng9/cCUSPBIkybypKNZnj7j+piXmiNNGsH6tOQx7vG9AGZRuyEljqqPi8PaKs22PNYn7l39HTBXXOOaQwTmymTBG41qNrZYYpaTHV4NfokPPzzYo0/P115HjPrw+9GgvnRyxymapdBGjptXEWghx4uJJg+0qPiqaTjMdK/uHPa1SUDUhgc6OqmY+/9u/4otf/oKzp8/4/u//Pp/+4t/RrxwffPQDnjx9Xw5j7hfc7we6puUQLd//7u+Rjjs++9uf4RKEIJOec5oQJ7765c/5/o9/wI/+r/83/tW/+O8ZwkTJGT0vKbbKM6MwquK8o1bLcVJMpuXp5XcwqzV/91d/iYkH+kXdlXJGOwvOMt3+mjO/4k/+sz/g6y/+hv/2539OzpHjUCjOM6E55ohKFUzi7Ood3vvo9zF+zYuXL8nNJTFJMsGckyi6TIU60VpD4yzHcaTRoGtauscnqAqjMvu7G0oaqTlwvumgZnLKFBJTGFFWklv7izPGZLg7BmZ1yTH1mOYC43qGu1te3b7ikTMUGzBthzIrvG/Z+p5a11ypc0J9j2M4yLWWLYf7B+Zd5N44vPdyPyopE0vJkmIiZsihYaoHqkk46+mbS3JuOU4PpCHjXIWamaaRcR7ItBjVYY0RlRKFFAeG8R6lI7laCoEhXpPyBGomqQFlf8ecwkkC+jbmL5vFaRKoi1b2JD2tvDns1+VrJDm1L6F331IxfYtrOP37t+WpIIfUjJwASy6vc42sfRMM9S1yevnZS9Uo5cEqUgbbGh6/3+O7FTdf/5rjww1xOKCsGFiSllKdSqHWhMbjTaXMkVrlNCdVEWXhUIoshqUgPdKQ85IBBFSKkMK10HrNqjE0FpQSN/U4jVAK0+HAiy+/wDWarlOkGNC6iMt1c4aqipQLuShyhGnIEJcTdZWRsm2E8FZU5jSRU+b99z5kGgJffv4c78QoYxYj4DzOkgNvLXOITDow+Z71StM03eIhMKhsSFFOnFobSjEcj4UyiyO6VkWIUrIuseOSqimO4ISmoKrQrRmIBXKoPHp2Qd96Xn3znCnLhlqQ2kVjJb7EOkOKMw8P90zDEdM4qqr89K//kh/+5B+y6jfMUSoSs9FkswgEbKXtGozVrxVoShlCVPTrLY8un9L0K6bhlsPD5yhTKVimmKkFTOsJU+S9j77LNA28ePkCrQqrVcs0HKiqcByP+LbBN1J5mGOReI/Fra810l+w6O5PvpxC5Tg80DUt1lsur3p2dyO6Vkpa7pkiuWO1DqR5QnvLqqvsXq4gHLg8e8z52SXGrfnwox+T5gN//mf/HNSRy8vHNL7j5osv0RjxHiTp2K5VU0ri608/RuWB73//O3TOMtTKer0hlUiYA0kZtmePWLcN8zCRssUWzXvPPuKP/vE/xeXIx3/556xs4cwplLIkA75tpIej0bR9Sx2vafXE1y+/RBtHzXCMhZwSV0/OePzOOVPJ6HaFX3mmCOdXT+n7jvFww8PdC8b918Q4gQKtqgTgAX3bUkthmqaFzwsMY6ZxmnGYJXkAwxzLMrHLlJDQhAJeW0r1vLof+PWLPauLJ/jtE6rqqbZB+xntdwxxRAVY9w3TPDBMM5tWIKhcK65tWRknYgVl0cXjVENIVVKaVUHpjIqz8EpFZKdd39DqDViD1WnJecr45p5h2i0CEnGXhxBwtlCNKP6sUcQwcTjeM853VI7sjhNFBYxNZGaUmjFNYBh/x/DRiQs4LbqnxxvvQF42hZMk9Ddb2r799n8Ijjp9H3gThHf6vFOVp9aSAy9LnmwW3nvBlU99zQv3IDyDIoQMWqo9qZpSDEV5jDWcX73Lulmxv7/h9vYluURW6w7IxDAxHg6MxwOlSvxtjJFaHNY4UGaZbkSBklNZvo94giVD5vSzi1s4z4muaVivPF5lyIFh2InOOBdyqQzjAd95njw9o6SEtYrLy3PCPDMdZ3LSzFMmh0LTeKkktRXrYHu24uriDAXM0yiR2Ammo8hf56kQY4alo9coCFEwboWjao12LTEWwWabRmAlIBQlZr4qEMl+N1ILtJ2CLH4NbR2+sRKcViXSAy1b6MnGUDXiVCgFbTWH48A0DqRSMdYxjBFlBDc9Jeuu+o4xRHa3L9jdXGAaz2dffc1hhu9994dkKwmlp17ajMSL1ZTRVZGjmM1c2+BdT6wdm0fPaDdX3Nzv+ObLl2x1g9JS1MNicCx4itH49SVDgmNS+KahNS1jGVBaUZJow7M2GC9T2jzPpEUGaRQ4o8ha4DLnFNWIek2TSHGEaFl1nlVvKFOmtaK4irkQJY5XYse1ZX93i/n6Sy42W26PB2JIGOMZHu743nc+4ubrD/n047+gholt95gvUyLFhFn4H2sUK2exWXwEYX/Lx397w/F4j6mRRxfvcAgju2GkFkvJGmc73KplfxQD3B/+3h/w6PKCv/vz/42V85x3DhsHLi7Oub27FQ4FhW86dE3sbl+RQuZs5Zcucy1GNDQ5Ry4vNjSbjrshMc+3+PYxJVt6f4YuM60xfPXJV8wzcl1QmSbxHrXaM02BhBgvvXeMkwTe7edM31qcaRhTxmhPJZIzhDETamJjDav+jJevbnh4iGweN1jnyErL9eAgq8zdfmAfjmxCxbdbDBZTC/tdYD/s0Y3GNJbziysuzq9AGYxx8lpahdISobPks9K0gMqgIhgwtsVqjVMdxha0bSnVEsMea1vOzy/wrhVPjFKoAmMYmIYHxvnAHPfk+oBxEaUjyhRiPAIzbaPRefrdbgpai3z0RBAvW8ISMSGF36WcPk5u/tNC/ran4UQOw7fVRaeF/G1PwtuPNxWe6vVUcfoa3nucsW9gJt5sYrnKDSNFO2LZr9pTlSWWiDUav/G01dHhKF4KgtI8Ul2ga89w65HpsOd4f08sCbQRp7AGbRSmVijx9c9Xs0wNagHKc6ryb1VUTM5pjJIbvea4GE8kUrfrGuEvqkRyvPfeu4R5z7pzPMREUIZ5TpRUsb7DuQY97TAmi3KqZmIsqKoYh8zubsJozfPjC2oVzfr+MFCXiJ6UCkobkvIS7Ws8dRZyc7VqKDhs30vO/HSgZNDKMo4z0yhREtpq6aJWEGtmXCaXslwHschtUBZNQRXaTzaFVLm/39O2HqsNVRdsI0UuSksNpvees7MVh/HIw/Gez3/xN9i2ZX+csf05w90rVt5ztulZrTrKPFCK8DqlFOZpIpVC06/pujVFrVidv8f54/dQTc+LhyO26cizXbKiIGXDHEViXbXl5598JYQtnpQNh+s9VgvP4oujbbc8uXjE1198Ts5B4iUWAUYFIc6VKK20M9RFfECcmfY7KI6usXzngyuef/GSlTX4piHmwjwHQizkBFZVwuGBV18knn33B5ikIWfmOfDFr3/Nl7/6N3R6x1nruHv+JX+3f0k4DlitmWOkJHHqlhRpjSHNAz2WTduip0KoleFwzxATbb+GIF0k0zxRQmQaElU3/Nt/9S+5unpMHPYwHagaOq/wxrDpVtzd3crztpZhOOJ8i1eGRkcuV4aHfUIRab1Dl8g07VldWpQaePXqjs1ZoV894ovPv+bu5QvCcBS5tRMtv0RACK4+z3IYwVqUMjRtix4joWjSmAgZrC5oJU7lUqQa1zpNVwzozMPxgXEoOC/8UB0PSNtsRJssXFnSjCGx++YF23WiazwPt4ndwx2xJKYYwGk++Oi7XFw8ZdWt2ay2zPNM1YWmXaF0IuYDpSRCuEephHMK5zzWtGi1wqozUhyX0rEjucw0psE3ns5bSk44A0YbhpgY5gfmcE8xRwp7mfpNJIQjcxrQOlO0Qf+Wq/1vvykoOf2eMKHXUNKCWygk0kDVb3clw7fNbqcF/jff/g+5pU/f5/Rx0timvmVYq1Uand42xb39ESKTFflsqRWFGIlCSnjjKFjmOTIVB+2WwEwpCW17tGlQOdHYBtesUEpIqTCNryEjtWxyNRfpWVAVKb3QYJyY+pb2GoV0uWotUBpFCEmjWMrrKjkFYqrgNCUnrNFMKfHN17dQChrLPGdy0dRYORyOEhFuAaWYxkiOOzSGMEdyEjLNe0stBW8tSrWMIRCSpFgq35NdT7M+59GzJ7St5mLrcYwc9zdszi6pSTDKXGZKrszzyELzEGJe5JYwxcKcZDFXxpJrJaKIdUmg1OIOL4ub1FAJsciN1zSgq4SUVYkPcU5mjP3DA+M80xpgPlKIPN6ecQgB4kCZD8uipJlLkUpJBLqJQZzSOWZRsahALZGHu2tcd8Z21bJ69ymf//VP6TY9ucI8ZYYx0jSOi6vHbC8uqbrycHggpcTxoClKehLQK4JqiLYhoLEKnBfeyChRaTkvfdYYOTVmCiYtvd9xXv7LYHTBa7C2oAgSlKYMVkn6qXOAUxznga8+/YTV4w9prEGRONu2/OmvfspZM3K1BVMjxEyJEwrxtUx1Xorv5FrrvKGOR4Y04KpsxNP+AK5FKYtvrah3DIxxWIyEiTAf+Xr/gnjcc9EriCMYxf31NW3T4YwRmKdKjlCNk5zqbaUagT6zzlQDj5+saVxhOj5QYsRoxdm2ZfdwS9dA82TDsC9s1or9fk8ImXHIYi5VGjDEKDlLIc5MYxSoUDtyyYwhU3JE10LXSm6adRJXUTAch8A0zWw2W2zWjMc7Qklo94Cylm695g9+/ycU7diNEzFVWt+R5plPPv65qICUTIfjIXJ2fqBtetabzXK/FdASdqh1YgwDqVZyDViXpM7XeKgNOXgSDSiLsxnvBy6852y7Fre+jjjjUQR0rXS9whwjcdzj7EBRB2J5QNdIqjPoLAGcuWCV4bd5/L02hdeL8KIC0m9JRM1bsE2p9d9b3N9e8N8+zZ+Cw97eBN6Gqb4VqvfmKwlxvchcFaIQkYpMEC+y6MG1FmJ4eRIC59SKcZZUMilnUlEkbcG2i64/4LyCkkh5EFeq0vjVimaeyKqQY5CbS0k8AeWNizulJf/cgNWarKtMFVXhnDiXja5YbSk1koOQer4xxCTuTmdF3vjF55+j1VKNVhRxmtBakYvmYS8XqPcWnSreycU0TUW6d2Oibxq8tyiVaTuLqpmzbYM+QBkCrmmpvifang9/9Hv88Z/8UyEYw5HnX35MmmFWa5QvNL2i5JH5eGR3uCXNiq6RzHylJVIh5cwcMzlLqmOulWqsmP5qhSJv15MKrSSchRAyOY80TmTE2hUa58U3kGZSkAgG70QBo2vCpBmPQaWBEnaokqFGak4UXUkVvFvYdmAeR+5evUTZFXfXD8SswXjOLi6kl2KcSc4yxZkwZy7Oznny7L1l4VHkXOj7NU3X0nQ91jq2j684ziPfvPiCm/2Ash1OWUyN6JIxSqGLlDxlCipXTF64s1KxxlPyTMkFsuV4v6PxYHUlxhnnLX3XYLXGLgm/qmbWTcvdeCQOB6bDLZHKzYtf8+7jLcwDVgVWjcabQtIZ6w2HOSxdFvJ8jCoSTLifsb1GGSlySUlxdnlJbdaYtmG/f0BV6NqOIR2J84iKhfkwsPaKMia0KljlmGJkypWaCl3v5d50ZqnXzRitmafEqoe5ZFaXDZsN9L34FKxWvP/sGat1x3F34GyjePrRU775asS5Fc9fVG5v9+RapBdEa5quIxaoRROiTMqwRI8Y8U+VtARgsrxfNdo4vBORzKrXjKFIJHqFrAZSHMlJk01idXVJMp6m2XJ59ZgUA2E8Mtxf89FH3yGUxBAiz29uuX84cNzt6JwVybqV+2LYTcQ8EssDyh1BB2IKci2YDpVbUpT1r191WJNwc4tzmtXKs3sQWO7q/F1qKZTSYGzCtZk2F7ATuRzwZkapvMR6aHIxgCenb6Mv/7HH3ys6+wTJnIjit6Wpkt6neBsGejvT6O3F/e3JQSkliZNvqZXe/v/TBHH6WPl7gYdQWG2+9X2Meuv7LFnpEp/tUGhCHDHGSFvbYsctRuHaDlc9NVtStNKyVqXpKRFQxmAbB96gsyWRJUI6nUL1GmoCZSqNdfJCL+5ctfw8MSYaZ7HOSkBXjJQi6YbGaax1xCytVtZ7vG9oXYU6U0slTpKG6HxDt9owhj3DHKhJym2c94DlcDxgqHhrUEZJln+ZMK6jay1hjKw7Wcxtv+HZ937M6vGHrJ68x1Q1WlnwmtI9wmwLycA0HhmjlNmY5LHdwDzfMUdAGZxW9OsNcwwMD3tS1kuJyUKWKmneSjmjjX19s1IzseZFsWOJqkoHsFKgLV3Xc3wYJb+/ygVr9FL+kgNr13Pz9ScinW0sq6ZiWsF6VS1kpBlPa8H2cxioUwAcpgjfEcs942EPIfJwE9DaopWnMZab589RWtOuekxjwSr29wPHOeJ6y9Ozx3Suobl4h1cvviANdxyHW4Y5YlPi6dUV3lru7u8ppS4ac01M80Imz1AVOSZKNjgtBKKxFdcKb1ZLwOqC6eR6xhqS0uzHwri75eHVl6zJDA/Pebj9ios+4FRGF03MmUqS/K8s2Vg4IwKMlGm9QxFJs2RbaWVQRXP/6gHVK5KeiDlzvl5BzaicSMOEyolWQcPSyVEhzRmrnMRlq0LNifVmjRpHgc00rJqGQc+kUrjaeuxKY/RMDmDaNZt+S7+W/P+Lsw5vZtbrme//YMNut6fperpVJuSZ6+uMUZGqxaMcYiFXR8oiTnBGY60lhQjWUUuS+9Ub1PI7x2qM1VgvvpkpB+GglKZgSAls8vQGkjYYv4I54TU4b/nDf/Bjdvc3nG+2PHn8mPff/4A//bN/zee/+piHVyuMqpxvN6SYiTmzOVvhukKJE9qXpeegp2sv0HnDUDXDONOtnBxEXUvKI7d3N8zTkUN5oJbEZvUYTctwHKiM+DYLuWwqqSZKSVjTULJG0dO4cxS/4z6FU+yqnJpOyqLKm0TSE4QjJPBvwkVvTGtvlEGSxmlfG9ve5hZOm8zp8+Xri6X75FVQvAU3VVk03jS4ndJVs+D+VpGW6AuJpm0F+smZpDI1F3TR1CyFO4j3CqcNxjdQIwmLcpp4zCSSnJDVkiqpFBipvzNGfi8nAIsqP2/KiXGaaGwr6YXG0XQa54zIMRfJjVJCOhnrQCWapqVxPUd1WNzKEhmtraOowBQLrfdge6Y4M+dM5y22c6QaRJHUO1yrSTUQc6AUTeMdvtW0Dt577xF2u+X6OBKCREIXbcnKCWyoO5pei1KjixjX82X5FWE4CqGaYX8YGeeZUow4WSvS8KblZFySEG0lCymu1RsvCboSQpQwv5SxynN+fsk8HGnaljQfpU1rgWbK4seYw8jxIfD155Fu1VLSga7RGBwpJtl4lgnuVLWJyqgsOTgaaGtG6cguZtlxSiGkkZs5MM4J5yULyHqDcrLZRe3ItuMwJjb9Y5689wH92TP2d88Zrj/n1ee/wFXFy9uR8+0ZRTXEWtFFYazj2ZMnvHr+khwDOcoGFlNF5YzXQrDLdVzRqiyJmYgyrkZCkErYxmk2raLVM7YM9B50jcJlFcH3JEASSpbY8rGEZbKHjEGXLHJmVYlj4pASI4nSVKbScJwSF7//Lu88fcyf/a//CyoFPIWzzrFq2iVhNZK1Rpuy6PA92prX93apFW0tSjvW2walFdUFlJ2JKQCW1jcov6ZtVmQU21VDzgM5PeBdoJZbYpixprDdGHLWPH9VqHrkvQ++T1GGX3/+hbSnqcLpVKatBhwli7S2VkVORSShuqKKxmqLs5K7lKoi5ErKEfnJIr5EvBXlXE2ZplE0q5Z5Chxr4Ob5lyRlefT0Xb77zjNinMjDA6kEbvc3pCjCC5Ufsb3qsb1BlQayYhwUulYhxJ3HLzEsKZUlBbhhmo5Lx7Li5ctvKJeGq8t3UUlge1MAU4XLzKLqsqan9Wc4/Zi+e4rV3W+11v/9so+QPl/ZEZaNQb8tIT1dyN/mFOSUHF9PA2/LUY0xbxL+lsfbH/Pma775nFqWVNa3NoTT4+3k1SVwURRHWTqIjVY4Y2iXCzYnRSlBemdDROWEUmWRjBUqYsMXQ5ooQIZppOZIbxetcM2Li1sWd7Recm4W0P11tLhiniKpacAblPHSWWwVlEoYZ8aQQRmapTM65oIt8jMY66hIJWHMhSlWEuIF2PRrNheP0KrQdg2NVzhTUNVgtGxUc4qkMBODyEibrlLSkZuXn+HXPZunB4rpcLYjHBLT8YFV69nt9pQK3WqLNZZ5HOmM5X1t+PyTj5kPD8QYmCfhVkIqFCUTkPOSpkrJ6JQwFVAFtRiiKolsK9ooMnX5fUlIWKqKaQ54lgPCUgGZk6hMuqbBUZhrJgy3pKgknsI5ybAqAllJTLJ4TiRHP0GWa0EDOkZ0gqaRwvk5xKUBr0q8dxVzYwqRPCearqXbdpjGo22Hdme4/imt2jJHT9tseXiYCLsbUpi5bB9x3E/o6rm8OGfdGYyyxKmiS0VlRWOsnGpOnT4FWNzPEjb55nIXWK5IFaMq3D//Ar+/Zbh7iasJs+R/1WqRlFzJ9rFa4VxFG0PbrcgpcZhGvIbOOyqVaQqEJJLtrltz5h9zfyyQN6TQcNxJp/QcZ5S16KSoQbg6rRtiGDFO0fpWOj1CYj8GjLP0vicri28aSqkMwwFXEs47jHKEKUIaabrM/cOOcXzA2yN6NTHu7rGq0rjK9p1HNA1szzTKHrm+m1HWQ5Wa01xnjLOgJc4dpWXSN1ZUXDVTMdQC8xKYl7PGtg7barzzOLtMuIu8mjDgmzPQiULFY2lRxDii5iN5PNK0GzoqP/jgHSCT4p6H2+fcX19Tx5mYI9NdoG0e07aXaDaveUprVqzXZ5SsaZoGazUm94BUzjobOOwPtG3D+cUFznVQLdb0+BoodYfSDUkZalEY5enbczr/Ho15D28fo9Xqt1rnf3v4iLeC8Opbp/e66MG1/F2VcAq5vlEXmaWN6jd9DimlRcpaX08HKSWcc9/iGeCtTUHe+famcSJ7F9jqJAWVkDxLziIXlVGtwdklc7xI4fc0jMzzLPEMJGkf825ZNKQXVSKsR3LMks0DKGewtoGSyKlQjUEpcbkuWyVVaZQ+nZal9nAMcSEQK8UgRKLzKKeIk5R5zAmBT5oNhpk5RMZpJGY5AR7HmZAVzarjycUVXdvTbFZ0raHpwZlCzQM5ZLzx6FqI40jI0qEtRG9C1UA43nD95c8oZUT1WxKWXBRlHAHNynccp8Q8T9S2FR9A1bjtis2jRzw/7CQzx1vRimcpfPeAX+SY1WiikT5vpQQWEYWRfr3gV6qcppSl6zqub+9QMS6tVgalTlHMBq0Nh8ORbiv9vcpBLBO6anQVyMiavMxrVXpeTplNShqE20baxmKcyaUSxJsoHgsnrmAJQrSEeUY7jXWKkgKHhztat6JfbVmtH4HaEqrDtJV+fcZ3flj5xb/7C7zPvLyPGH+GUQHfbvjgO+/yi7/5S9kYi0LnKnEkS21pZdkUjExSZWkMs0YmBVPBaoOucDzu+bu//guapmEed1gGfC/PuBSBMWuRHC9vIHs5sa4v1jjX8vXX34hgwltCmEkJ4cpygZDIRH70g3/AMOx5/s0L2rYlDfesTMXpAmUW30bfsdmuycUxjgNzCBg8fuFfUsnYtkcZz2FK3NzcUdPE2ZmmXylKnqg60249TlfiPDIej5guEVQSmJjEqunxTc/FWYsylm+uC01refHqWmpmjaZosM6CSnIoUKKCUMqgrYc0k4nEJZ1Va+ETDBlbovQtL+uWRqTR5InOZbKNpFpptMXWjEoTOs90qvD44ozOasgRYwpTHGiZ2NhA9BE9jxAq4WApmw3OnuH8Obpp2KzP6PqV+MGUphTwXqTlKRu2mzOcW6HInG0uMapHqV5qU1Ul2wNaDWiOYCPVKBq7wZsNVm1x6oLGXfxuN4W3T/1v8wRvR2O/flvJqFqKeANOf96U5Lz5mt/uTFicysvI+bYs9TXRvOhdf5PI/tamoPUyfotpRSuJZnDaSFvacgMKpCEklFo09XnR1WslG5rVCm8UpFlOUUOk0Y5UKnGONKaRnKSSUFX8EDmfIhzeFBJppdCmYquY2lIWcjmkuixCGuNXVJOoyjCnTM7QtK0MGssC6r1jDAmM4p0n77I6e0S33tB3vagc6kzKUuSS84QiUbDkVChLkXouCVwEXfFe4b3CqpHh9nM4rIhFM06SAdU2K2pzjm/WFK0Zw8icxJRVyJw9OufhpifvI8ZKxLJ3YpDy3tJ1js1mRS6J43AQQhUZNgMQayWcUhSUeCCqAt92PLq45MtPPmEOkUarJRJFUkutazG28N2PPuSLb37NGA90K0vjhE+YZzEIaSfRyAbRt/PadmiwvqOWTEmJrBLKWnKozCHjreQ6NU4STlOR4pe2cxzGA8fjAXsW6NqOplkx0VGqZkoTq66l2Uy888GP0XHi6mzD937wLsPtN3z+s7/mZ3/7McN+pMyFbe/RRjJsnFG0vjIF4VSMcaAKKSXB+61CaRF4qKxQRpFDIc8TU4iUPKN1YThmrGvoek+tiVySENzK0DSauWRiDWy3l3T7FdNwZM5ZGuSUbD4lJQ4Pd3RXW370ow958fIb/uLf/JyuVWhr6FXGuQAEnC80XQWb0Fn+v2R5Dl2/xjY9r+7vyUWRUubmfsdXzw9sWgGfayn4tjLlgbvDDKaj9WtGrbDKs2pbhkOglEhMEw8P99ztDN9cR3YPGaU6Xry8ZooJ34nKK9e63PcSD14y5GViUiisa3AWEU7EjLaGnDRhaWMzWuGsQOVdp5jDAVRAaYspEKdBMPyS6L3GYlm3VgyaWlPzzPHummm4ZuMVxVSMLsxqJk17drd3dHnLmXX4ZsVwDEzxBdZoNu0Fyoi89phHqB2tb3jyqCelCa08Rveo2lKXtcuyoeQ7arGo6kR9VRyqelRtMLWnc2e/1Vr/28dcnBbmRXL6Gsp56//LWwqhUiWjJKUqQVPWYpecI5YFvFRZEEGRF87BeY3WVRrS6tJkVbVIPN8iketCElIrqioZDZVMJ2JmLdQSl15ah0YmCIoSXLvI5OB9S99nMRvVJGNoSeQUlpROSR6cQ5YEzDhJtV9e5I3GYJae2AW1piI1hijpKtYaiq6SY6OlkyEW4S40QLbUsBCzaEoVDDbmSk6VkhMxTNIVrSvrteXRkwuevPMuTX+OcS3UQphHwjyj6sg0PqDyjFUQUiQHIQE1hlSj+AQqJApeO2oKzGnC5YlSFMQiC68yhDySopEGNbHYklOFklEVzjYbrnf3HIeJxkNrwZqCMZGaMzFqTttkVVJOpGrFKpkMdJW2tlQ1znqK8rz3wY/5r/7L/4r/+v/xf+d48xW+N+iq8KYhpYjWI33XoIlMxx3Gg60FlunTaIGadJVolVpkkaqAdxaHxVnLFAooj7aacYQpV6ZSmKujVDgG0fSvWkuHIx4DU0w0fs2TJ+/inSPEiWJ7KhnnPIfDgXTMlGw57Eb+8B/8Pl/83d9y1ilqTMR5Js4BUysKS9/3DMOESmGZmOTStlaupYxCeYtqGrRtqFEx7iP7kNjPiWOY5dBQC62XK9A2itX2pLSpAtlVTTWaYiqXT59ydfmIr778hnmewUl/uXLglMZoRymG8f4l/8N/818zzCPrdUt/1rL1nnD3EmIEBxkYa2KYJxQG07ToUimpMI+RlDI6aYbdwBQzu4cDpQgUPRwyJWdWa0s1SmSeP/0Zz979kCdXj9Gq8vzLrxmGwDAN5Op4cf2S20MlqhW6OafoluO0I0h0E5vNlppnMhljWtp2SwiJcRwwOuI0pDITiwxjzjWvI/ZzRDo8KEQt6sISK+M4MYcZXAvFoZMopTbbnt5a1u2K89WKOcrvOqRIax3d9oKVT8zTjlw1Ohb2+3tud5HtqNl0PX7TMsaZXCJd1zHNI61fLZlyYHVLzbJUN36FtZ4cRcduG0dRMyWv0eVSYnFSByhMfUpjnuL0JdasqPV3TDQba1+nnL5WFvEGEkopvQY9RYMvF2nJmVgSqnr0Iv+yVuOsI0YxbpVal8VTo8lQJQktpoVOVqKIUcYu+L4FJFyu1LqMeJK8qpCUUuEDilRg1kBVFow0HyklJScF2ZSsdyJnTWC1JeVCmGdqFYlaTpX9/p45zmhTUTnhlBhIahzIKqO0XXBoh1KaQiDXglkmBSGMtXwMmlxgLmJaoRoolpxFjSSqnMo0TeTS0rYdMMsmh0wi263G6AM1V1AtYZiYpoEYjszHO/K0l35qpXDG4htLjXKx5hLJquKNAeO5evIum82KTz/9JbXMrPs1vsDDfqDMltqvSGXG2oY4DhyPg2z0gKPS2EX/rKs4Mx30rbSjhZjZDweqaSjKMsbMuu/xRnPc74RPidA6R0qGqhpwa9brJ/z8l19x8zDSak9KAQdkVVmve6wvxHTkl5/8DGPkkiEpqlbMc8YuJT59t1qKXDLGObqV43K7ZndzTwxHwpwWo5tH+4Z+1bN60lF1w4uX1zhteXZ1wbS74f54wGkREaSYoWief/Ulu/icH/3Rn9CYyuF4SzzssHGk0YbDPPH1rz7m5rNf8eXhmsZBmB7IIWIs1KpxriPnSQqHbMUrmSZLiVStwGiqa5l1S6ott2Ph+X3ibg/HoWCUdCFYpQhzXboBRpy3dL1IYQuJlOXQ45qObr3meByZjgOqQpgDlYq34jcxVBwFTWIcbum0ZtqNtJse5xtS0eQJ0CIv3o0Ft2m4fHRFYz37mzvCdMANiTBOxGmW1rpScKpwsQW9iAUMihyQgyNgtePlr7/ieLOX6+d4JJXCYSrc70f2A+junOp7avLsjgO3u5GSKl1jsIBvPNUEtG9p3RXr3nNXXzIMz1FmJKcZrzR907DuGuZ5IleRChYlPExNJ49TpOTI7uUXIgQphbUxaN+RuGDOGtttKOcRg2K9WnEz7FAFmmZF1XuMNfRth6qZPEfSuOfw6lPS4w3+6YakK1pLK+OcR4nq1wZjT5O1QusGjaFmSaNOKZByJJdAweH8E3x7TimBUqBttnh3gTMXOLNG/R9Rx/m2CuhtSSq8IYeN0dQqWKZINkUOF1MQA433+KVXVGklIVtF8n9Qb9rYRHZ60hIVJOBHlCnYJYn0tQJKVEBCDmcy4lqMKSzKiygTQbclpywYP5qUo4RrkVAqSybMIlqopRJjIAaBvOZ5xhiH8w05T5C15KgghdrWiEOSKhn6JzhE5mOkd2IRa5UEMeeleEX6FvRbYYOlVkqV6WR3mKjV4IynaVrBXksgzDuJldAerT1hGskpMoeJFCdxbFpw1mIw5CkyR4lFVhpab1l1vYQZasfZ+SXP3n2fm/trDuOIsa1IQ2PGlOX3WmZefvUlr65f8c7TJ7RO41pH7yNnawWp0jmN15V+1UjZksk407I6f8Lq/Iqbuwec1oyHPXmYKVqitkPSlFxYrRps2/Czv/1LHh4eKGmkX1tWDlQKKK04DBMX/ZqSZ5zzxJwW3sqLEso6EhKTfByPWO/INVJroe88MDHPe6gK6x3TFKDZ8Hs//Cck06PbHu167Me/5PHFObfffMnT997n/uXn3F2/pGvBWpiP9+TpnvEY+ewXf4HSPTcvb0jDiCszrs7M054vPj/wzrrl869voXfM08R23dA4wzRMPB9fihDDaKwzYDUxKGLOpKIZQiJPkakUbh7ueX6beTgKB6J1pWvBcYqwVxgNca4c9tIrbHSHMpGcZpSC8/Nzjocdu1cPGF0ELjUOaxXjGFClYk0hl4LWkcbLa+Od43i4QQcHx8BFL9duu9qSs+Ly4j3eff89rNEcdyPH8ZbpMEMq5BRxjcNpRWeRe9/Kaw9i8CxKWtWs7zAhMRwHpikSS2U/BGKRVsSz8xX9xTP2c+Xl7Y7Pv3hBCEX8LmpkOGrmbChG8eGjx9hmQy2V7facxo2MxwONVawbw8W6xaBQBuaYqFVLf3RVoE5R9BWpikmo6lA1E0shpoEhDGTlueo8xqXl/h8Y4z3DfMdxTGgdaZ0hF+lAsL7Bp0jMhhIqjW3Q1lFtwxBmcpZkY+sqeomG976RoM9SCGFmniVkUvpsZLNQqsV5gW0FvHZoOoxeYU0nndC/y03hbd/Bb7qQT9i/tfYtglgu0hCCKFPC/Bp2krRU6SLVWi1tV/L1Ug6UpSReYJTley79BEIbOjntU1/j9XXxDVQyqoicbByPxDgwh4HVaoOzilyEt3DekZLUHSpd0brgrCcFRdUaZ9ySXSM1m0pfMPQdh9Zx8408J3QS7XcBXTXON2KaU4pYRUJyUo04azBZUZKE+MVhlheuyhRmkxI374LAGdtQlOY4JiFt146u67DKkeMsRpySUKqS4oAm0bQWoyoTvCZjAUKMsjHnIi5TLRNRDAmtHNfXd6y3W777/R9x+LuZ2xcv6NcNIVaKzjQpY5xClUyYDszHPeNOoRuNdhucCXzw1GO0o6YkUdRWcM1u1eJWF+hug3Idj9+7YL/bc7g/YldXaOuZQ+D//M/+Ka+++Yq/+ev/HbfwFq3VbDYNvo70nUHXhkfPPsA3nk8++yXaeQ6HCEsbVa0iwTVWAxGl5fXWiPQ558A87dnfRGqVwL3DHBkS2L4n+zVRd2jd0fg1j9/5kM5Ubu/u6K5WrFYd494R55nNRlPGe9LxFV21fPI3/4q7u4F5jJz1axpd+d5338ddrLj+5nPq3UitiRAzvtGs1y1hCuyHQInQ9hZ3aixUloomxETIlYeHyt1xIgIPA+wGCEn8kn3n6Fxm02mcrjTmRNBUxkHAJ+ugdUJempJprCOMI9evXrFtW1zXEZN0Hve9RMKwyHUzEe81pUiuz3E/s9tPnFlEMp2hcS2r8zOePX4Hp1se7u95df1ASBVSpMyyYHuV8VbjFum4MaLwqQvPVlJCV/DWMMeEcxaUZX+3k15zBB5Ga+ZhYJoS1Mj5mWOeI/NUCHPAqgzVE43HNj1Nt8IZg8HDFoaHQKMHels5a1rCNKOMJsYiALB+63BqgCJdKVRZ06z3tG3HGIJwMSReXL+kOodrO7pVj7KK9cWW21evmIZAWW8BTaKincM2GWs7mvYCb7co4yjGYpRGuxPoIoop7y2Nb5eu6+XaNae1VjZspSrOG5y3OGcx2lOyohaLVg1KOWr5HZvXXjuZlw3gbV/Bb5LQxmica2UMXbwIxlhqkWo459yygSzSK1MoRSYKSZeMi3HsjedBAtLU603ilLH0JpUVyUpXi8tYW5wzTNNESgM5OyrzUp5t8I3UXmqtyDULoews3jjIhWwDThuarqFb9dRaWPUtZ+sO0sz9K4mcqHkmxUQpgVoNxiisVeQENdcFDqrLRbkoQVKkpCghDybJHBQkMM8YjdUetCMWGOeINYmucXTe0XhLNRDDyDRLwFWMZXFEt5hWoaohVzGNlCp655QLeaFh1ps1Bss0BUpRzLHwyadf8KvPPufF9UsOY6AfK9a2NMZjjcZ5Q06RTWuIa4dOR7quozMBS6DvHNYUhrFwONQFG1doZ6naUrVjKorWrWg2DWdPFLv7O5zvePfyEc9f7Xjx4tXiQB9YeY1SGasC51upULXG8eF338P4lhd31xzngGo6Yla0zRmma0jhwGG6F0IOJafbIbDuDK0xxLnSesc4RpTpUN5BtbRn7zDrFbu5SpFRrPTbc178+mOa1rHZ9OxvbnEO8lwhBVQcmB9eshsCPlXq4Q4XYRrvsH3Hq68i5+dbWm/Zvbyjt/ICnF1sSLUw5/za2Gd8S1GKnCMpVo5DYBwLY6gchso0CaxhjWGz0rRVojxWq5ZWz6wbUZwZLUGGKM00FoZhROnC5bll1ZllkpdJWCt49s4zDIpXN9eE4YhrGpxx5JTIcxCDXxUeJmVpuLNeMogq0tscw4huHNcvv6beNOz2B6ZBDJcKRTWgnZzGnZM6zBCTLN4ndycQQmFUAzFn4lIxG/NprUEOcsWQilRYSkRM4tFlTy1Zpoo5YbTmYUooGnm+FEouOFtpm5biPY+3PZfrhv3NNc5YgZFDkjVnUSYqo5G4dV6T1ykWQs4MMbMfIsp6mlXPOMLxi1d06zUffrRmvXmEiRM8TKy6S/r1OSVXXFQ437GphlQsm7NnoDrERyEQYGUxzhaFsgZrW7xvqFWqZHOWQ1fTNNSiiFFKx1arlfi+UOQsKQPyw4s0Ob9lBP6dbAohhH+vHvM3DWqvk0kVWK3QzsnC2nTkHIlRHLla22UsEuOWIqOK3CBaa3TVouCpZTGjSTGF1ha1nCqUlpnhJIVlkZbJDlGXDUeh9IgbinTG1oQ1ftl5LUo3EjY2zWijqVk2Ie88Zr0hx0BVMM9y8XrbYtcXvP/h99h0DeF4TZx37PcHhiFQUhLeQ0mUQDVIuUZKkgBaMjUm1HIKK0Wch147MetpkUtqralLoFuiMkyR+4dITbDu4Hzt6RqDyhFKoXWKmDKUiDWy6ea5EEIhZ+EDc9GkkvHOoLRlGiZK0YxTZIpSK1lUIVMWt7FhComugCaT5z2UwsW6oVcbdE2svFqMNpEaClOc0cZgrIwjCotte8xqTTIN2TQMWVGw+M0VPmms7Xj2wQ/5f/+//numww3eVLzJOJtYtZbGFbwJtN6ijObjT3/KlArf+/GPUXaNac745SffsFqfcXV1RQwHvv7mVxyON6gMBsNxd4cqEd87bOsI80BMoNst2DV9d053/h0eZmg2F6zOHxHDxLy/5/bumtZWht0rjvfXuBo5u+ooKZCOO3Rjme/u2B8n2qyoGYEDQmZ+yOxyYLNZsX33HWrY4xvDo3eecDwcuD8EhgJxhnyI4j8omTBXStKUrJlCpeDQRsl0UEUw4a3COIlfXzuD1zONk8V6XHT4w1CZgqjstmvDetNzfrbC+Y79sCPVzP5wIIbI7jixObvin/yzf8YYAp9//At0ruhcuL2+4+rZE8Yw8vz5V4JZqyL5XCqRysy8H0i7V5TSM82Jukyn1imURbpPlkgYbyR40CiZjkGhLdIPEhIhJly3QlWWxU6TypGURamUawIVyClQ4ow1lrb3nG8dVkv78vObgZtD5Kd/82/53kff52K7ZdztWbWVru05v9iy6T13N/fitAey8mQFKE81BacVaqm3XbTwokp0ZyS9hRLAObqLx7Ta4BpPu+rR3SXFNWiTefT+JavNGrQhzpEcK9Y2GN1gmxWbi0cU09OvNySg15VxeFjUg5F5iuIyV5ImO47S3CYQl3ANRluc7TG6p6ST7tEukT7ltXn4P1Rc9p+0KZy+1UluJAvXokZadqBapONAVEWzjDla7JmnJ1GWvKFcivAKSoKsUoaUgbq0bgFFRoVl01FQ9esNQsywZYFb5INKzVCLlFophbWWdbfGKOlFPhGEiiQqCauZ50SKFYcmzBljskAvylNLZQqBHCQ7qXhFYxSrzQZbHzHYxO3LAykI1zHXWTTwSnwQWllqztRcSSFgqkxDtRQUSxhakTC5SiXnQqGIqW3xAqDkhZ1jZgqVmiOrVjwU3nuR0taC1omUKylmpqkSs2aaMmAoRVRDIhwSG30JEed6ppApqhIzGGeJKXJxtqVtW8Zlo6tpFCloSngiShVSmMmpME5gtJxQSynYVvPo8hEJzX4IaCPNeBmJip7HmZQK8zhxOI7kOPGD71b+0X/2T/jrv/z/MB1HWmdoGqAkGmuhZrkRXCHUSETz2Ve/plk95fxqzdmjD9Cm5+LJR/SdAb/i5vor/ugf/THrruVf/4t/zny4Zn/7FcSIU4WQ4OF2INiOJx89o7ozjGlYbS/RxrPqHWXY0RiFyxFVZy62DaZIbELVGpUr434POeGAVddKaVGqkrQZDHm2jLry+KJDr6RMaHN5Rbs545uXO8Y0EbMiDplpTuKOz2AVywYLKi3KvsVfQUloXbGqoIlYJempRlWJN3GKkKXXWCmPsZWqjPRE+5Zuc844JZR1fHP9ihIK3eqM41T46U9/iXWOYhuG+UA+jHjf0bU9IQW571QlVkWkQMnSXlYTFsV0mNExYcqSe7X4UnKVA1dC0zYtDaCqeANSFJi2bSoZRawwzZP4DqxMnE3r0Umku6ZIcmucJoyueFNReZYFrXE0TcfTq47NVnN3N/D887/juF5Ta+bxozO6J1vuh8D9fmAfJfKmYEjOoqyXYqQQwRiUjsQ8Y7SlKIVres6efBe3fpeiLNo19JsNylqatkFbwziNzLXQ9p6LS4/xDrRioyVyXy8SWZTDNh3VOozvF4NspmRpYtztH5imkZxhniMpx9eye2cbvJPyrLIkPNSi0TiWaj+EHBXn+3+szfI/cVN4AxFJ3IR+E0BXl6mhVnH0Vk0MUVwCRhY2Vd6IV4WLeDv19MQVaJT2UiBuJTpaLR3OJx4iFYhZ4Akx0ZU3UtUiDtA3sRkFbRrOtg1VnUhgSDGTooSDxVDRqsHZVsB2tXiRlcY1nVQa5swcpafWWIutDtW2qH7FTa4iL1y+f04J77SMrUX6qq3SJPkBKTmRU6KWirMG54xsFEsbXabgG/E+VPkkatXECONcsb0nVccUMrkIvxBjIufCPGdSVoyTVCbmIqotUY0papXAsDInTKlIIYlhtd6yObe4VhJcm84zTxNxDBx29xyO1/KcUJgCKiaYI5klaK4WvDe0XYdRjs3mHIzhOLwUU1aR9Mq5KuYsqbS3t7fs7u5QVfPrLz7lJz/6Lv/kP/+n/Os/+59AjaQ0v060TDFwdnbG+dUFX908l+c6DriN5eLxu2zOP+QXv/yaUDta3VLqlt3hK+6uH7hNz/n9f/hH1LDjz/7n54xxYoiZXOEhZFS34XuXT1CrK6oyTHMihpF168lJphWnNSYFqPPiadFo4zHWMoyBWjVN27Bqe6wyxCmS58x8HEgpEyZL6wKrtePJ46d06w1ffvwpyXiickxZsPQpKtrGYk1CUcR5q/Ry2luuBSrGFKxVWJ2lj7lIhLlRSg5FRlNzwXrLD3/0E+ZwZBpfoq3FuoamX/Gw/wxtBdJ9+s47/N4f/DGff/WClzf3vP/hB/yDP/nH/Pz/+6e82H/KfHzg+OkdVReqitjWcn52jkVgzDEFGt8wD4GSpHqSVFlk/kslq5gum0bh2hWuaZmPD9SUTgsIxi2mvVhFxmoEujFA40RxGGKU6TtHaoq0K48qGWvlY6zKkEYcla33rB55doeRXA6Mc+T6ZkaZypQ2aKWY6agG+tWaRhl8t6IqLeuD0aR55OHhHowgHqvNJRfvfg+aK1KVbC/dNjRtS7fqQMNwdwe1YlYrtPeSNaUNVRnQks5KFigqKSsSU2nPJsaliExbQTWcrGcxZpQyeGexVmONf73wO+vxrqFWSKUsPhxeq0VPf3KJv9tN4e1o69ctaG8pkk6PeorAKCyS0zcQk9FvdyEocq7MNUrmiD3JTRXWmaVM5/Rk6muXXy0BKEsgnkAt4vpcOI5aUVVLGF4VUlkbL5uKMmjryLlitPAa8xwpVaaKQiUVkdNqLNaY5Rct0RVh3DFNgVZHvLOYvqNxHl3fqKTyLKQVFVKIUm6iFd5qiFCQ9i1jFdYYrBGF0qn/uSLdC6pWMc9UBDcsijFUjNUcg4yGYVJMk+io53HpZLBQqsFkgzFSS6qUVHkqNDlFcqkSr6E0q/WWx8/eAa1lLNeZ/cMdcZ5QNWNqJM4jMcqkoTN4pWmMxmCX37mhVENIhlAzn332OWjFHAohD7hg0N0FKVVCVEzjTBx25GlPyYWvvvglP/nJd3jyzrv8wT/6P/G3f/Wv8IjBb5wKRhmq8uyPM+MYwXd4L+7YiEU3Ky4ev8OXL254Tz8lqZYpav70z/6UlZ+5OuuZjjeYTpMmgQKnGeZSUDWRlTRxKeVw2nN1doktibuvA7UkutbRq5bAwDAH5jnx6NE7KOd5uL1hSpGm7SjGo4ynjIWYRHmHVlhb8X7NatUxh5HnL17ysN+jrUfbhjkJDp5rwboeU2cMkRQzmSx5Nk6ku9ItrHBGTtrqlKBSpUBq4YjJCCZu20wxlYJcy8M404cAVObpiM6FGGd2ux373QGtLPf3D9x+/gWFhGsSZT7S9g1N3/EwJLRpcOu1RMSohnXTMB73FHUUZZ5aOrirwmgnU7Gui4JNxDxUlvKg5eDHIitG4lG8rijrqNoQpwDaLGZYhcqVNE8YKo2Rele1fOEYCziLqQqlMxrFxcZSqmXoGvZDYEwVkx3KWILyrDYbVL/BGIdpe7R3NEphtYI4sXlWaNsO16xwTY9bnROUwZQMWiAuNFQnESRJF7zz2L4hlkrNFactzrZYJyIZpQreCRyvtJZWNiRdWVlLLWlRHHlCmJmmhPcOaz1ayWFW1teCswatM+akyKxQa17iUfLydhaP0f8Rm8JvJpn+e41qyO6plFycqp44AV5fsCe3sXAQwupzogNyoVSLdguOVhAZJRJdHOLS8IZGG41SciIXfE1egEzBaYO1Duc7pBhIsHStLN26k0A8ZWhCIiXJZk8loZWXm20xaSnkBFYrjOPI/fEGr2Yebx1NKXijcEYRYliInEoOshKXLDHOCoM2RtK1tUhXGy+90QqNMY4QE1oAOon5zQnfeJGnZZHsKt2hbIvtr0hx4rCLDGNgPM5YZSUqWFtQipDTkg6qpRe4Vmpapi/FIgGWPPqmaXl5e8Pd/S19Zznu72isobFGIpHtMicWkeZZU+mcwyhNUTCnzJwTTbfm/OKM25tXhGmWU3c1TPkOFzVF94QpMh1H6nTAlUmIs3DP57/+mO/98Md8+L0fMRyPfPbTv4QQ0b2j6Mz1zZ6YI8pqiRovFTsVbm53NOuJ737/B9ze/xUvb67RRrNar3i4GbFl5LoccDphlCIizvIAaO+IVTGnQuNanPb06wvOLx5Rp4HVas1tymQjk+JxmJnmTAya5zc7qjU8++A73O/33O/2pDnjdWAaJ4aQZDGwGW/s4iPIhPHIzd3XxATjMS0HAIVRhlTza3myXqI/lvMTSi0buUYKVpQAou61QXJ53UMh64JxhtZkHo4vWK1aLrstXdew2WzRCrabBhV6yjRze/2KHP+Oy8fvc96subm/4d/9xb9hDvc0dcJ5cB5KDXSrFmV7qvccp8Af/xf/F8ZvvuFv/vLPISnmUklzISVY9YuMenEJp5yZx5H7+1spmaoBq4X4LlTZFFTFWUkhTjWRo4hQtNGcolA4cZcV4iwn/1IKscq/OS2L7XicUFZTjUc7R9esKKYwJ00+ZrYXF2wvtvSrNbFUTNNB05GUxG4nVTC2w1uD8x3G92BbsvVoq7AlyYHLyoEqpCBO+kVymFIlneRVJWGq3OPOKqwRr8FS9YJTEs1RjDQl5ixKtJSSFGhZt6wXllKLQNY5k3NlojBPGedaQRiW3LRa37YPiNntd7opnBb909/fMrHVNymoJ6K3LCeABe2iVqlJrHVB95eUxlorMUcZxZavb53FJPvaIFdLXTYVyU9XFdTiHi6linxOG2rViwFMfil6GcFKzTSNLMIpZYw1KCN65NYYweJzpkTBS41C+mxjXvilxTENHA977odryuBZq8g0HqBGKJJs6J1Dm0QtgpdqW3BGOJCsq6RwUjHe0vkW0OQkyZJL1rdsbimivEKrJYfFtrimxbUr2tUTKbVpLjD392RzQx5HUYLkWaAHrd94MKoCIpDxxtBYjbOnUdaijGE4Dtzd3RJmT5xG8BrbOHQRd6cCrNESDpcLJYXFWGUIJVCd5aMffY8f/uT3+Jf/3T9nOk6UKMTpNN1Tp0xWnuMYRbRQM41KNE1GlwOfffYxc4Hv//AnfPdHf8Srr58z3z1nTkL2xTgJNzFUYo24UlldOr7++gV+8w6+P+fu4QZjCs8ebzm/3HL73HN1vuLm5htUY6jKUXXPME5EFFOyHMaEsWva7oykDK5bE4uGDBdXT/g4JG7GB0y4g5AwrmEaM612PH76DlfvfcD88hWHakg5cdgfyCGKqXKBN60upPlAo89Zn20ZDnu8s1AM1/GI0+C0pqCZjyOuNxQtyjUlv2LssraoIlCsVosbF41ZDmAhIoZPbzDOsO4UppmxjWbVn7FuV6z6FWEeWHWW/tEZYb/HpD15PvD8i8/43g9+j0dna6yv3N1NxGEmzFJN2q4bVpdrfL9hc/Euphu4ffWSr7/8DNNa5iEwYLg7ZDqnOevWcs+Fo0jPlYaSSdOAbgQairWIDNMY+R1USEsYYM0JpSz9qsW4hpBnpjkQo2ye3mhKKsKpOItGUhJKgpADIdVlAsuomrFas9le4PorkvaEpEjHTDIFjGPTbNFtt3g2NClMaCMFNVq3VN1TqkFlTedkSs5LcoHGLvE6hbNVjzYOrUUxaJQmp0yaA8dpwmpD4xtZI41AzSpHkhbvVF6g9dPpXmtN23aLYS2/9X8VrSyN77DOEWNiGiNumUaosl5qbZf3f9clO29NCMDrTeDt2Gs4Ub7q9VunQUIucJkKCifl0pusm1RkAVGI/LRmlrIeI983L2+XsngTZPHUSnoEpEh+wSdred0ZnZcNQWkjvyAjYxdaLad2R+MMKiVRHpT0GpfVWkjDWiJGa9rG03jHvI883O9IKhKmo1xwVuEb8RKUHAlzoqq0TBrltft7vVlLLWhdZLbLi2dqJcZMCAllNHMApQtd12Ab6ZPWxpBS5frmnvVmzfrsMcqt8N05adhjaublq6/IaUCbvLxulRwKKWaMAm9lMyyl4L1ltVotF1sEKinNkAspFGJNtN7TuZaUI6TMwo1jdEYpTZgjVVo8edjf8PLrL1BKSPRaZELRtRKnkTlLp7MDGi98h3Ea5RQ5Tvzqk19hmzXvv/suP/zJH/KLvzwwhz0GzRQUrbaMQyEpeIh7Zv2Spx9d8rOf/5z//W9/xnA80jWaeVxxdd7S92us1rTNGUolUlJSRZodIRuqWpNrR84tSvc4b7C+wdiGUEfafsthmGlrotOGXKKE0bUd51eP0K3ly+fPCaXy7IP32T/suLu5g5IFTy+8VtHleWTaK0qa8bqSSmHTd5haMFpO+41zDMNAyZVCXbKuDMopVCi4JfZEUTFKNmiKTNtzjK8PTafIF3HtF4yzAptWCWw87O8Iwz16PkAMOHVyz1c++cXPsa3lOx8943zd8XL/wNn2kjEMrDYbUlZcPX7KwzCxvTzj5uaauQbs2jFlzXQw0DpM4xjmxBhHbE1YnbGnWstcpctgEZRUp2iWWt+akkTeaIGJUJJ2EOd5OYgqGm8piyxzDkHWAGVJKQhMjANtuXp6yd3DPaVC26x478Pvsrl4l+3Fu1zvRr54/pJYC7kolDGgHca1tKsVzjl29zdL5ESLch2HKZJqYr1ekQJovaQcU6FkSlLY2nCxfYR1XhoF0VhTKSaSdGIOE3GehA9pW9IcGFNAKY2xhkVqIpuFNiJaMQ7jjESfJ+ml1tbjnV6czh6tLNboBYPTr2Ekyumgbl6vxb+7TWERHpWcF9L3DVegtRF1xKIEOpHSqspCeCI6jDGwLPKlvpV0WuS0LC5CLaF0CDQkp9y6YJAZcsFbJ2QaVSSvXtQ6oqOW0XWxIJJTJKhThpFgVE3bkXKlxgTKYK3BOEurF/w+BSGlU6UowbtqLTgqvTdMulDCSFQTmRnjCxaR1zojksusExhHTkUMkblAlY+xriHFSM0FbMU3ZmmSkjrPnBMeTwjx/0fbf/1almVrfthv2mW2OTZ8RmZWZvm6fduRRJPNbjYpQpAAQYD+Sj3oVQ8CyVajCRmiRfL2dVX3VlalicwMe/w2a61p9TDWPhFZbIi3heoNZFXEiXPOdmvPMcY3PoN3mr5vZ5v/iTpNbOKOcXfL6YOHLNZHkri2OmPYb2l3A3fXI94oyjRJFKSy6FppGk/bNgxjZAoZ14r2Y9jeMu3uxNW0amzTUHMQOmuSKSmnLEtAMdqACroW6gyfWa24u7jgy80dhB3WFLSplAC6ZHTNLKxYjRQM1rdgHGMs5Cj3UdPEt99+QdcpjleOjz99RN1ZXn7/kiEVlus1v/zFcy7vBujO6E6f8unP/j77ULi9u6OUyH53w93NBVfX77h5e8t3+2vOTjpq3EAaCduRXCxJL5hUx6NPfkHpjyi+x3uHKoYcE13TY2zCuAVpuKOozLAf8EQenh2z6BTGF0pInC2O6JuW1xdfU8aJVdegrGK4CWitcNWiY2W83VNDxBjDfgxgHF23ZEoB6ztS3mIM81IRofVqsErRe3sPRdQkXa9BUXNmmiBlITUoq4lB413HtMk8Xj8BltzuKvr4hGJ7xhAYthuaEtAxY6vCo8gpol3EAS+++h3LtePp42NhGEXFmAJDKrx++ZIxW9ZnDutalOvpVw/pjzW78BJnR2yOTPstpsQ5axtca/Be7L6NkYMvlCTXCYmYsggstUFludZq1nPDqClJi8ldmXCm0LrCwjtSFFfcsYqXVayF/viMjz//BeXrL9kNez750Wc8fvKcVDtqUXTtguVqRTKFhKi6jdYsmoZV31FKYbIWnR2+aVFarnvnHV3vZXfIfJDPNvDSuWuMde/dmI2FmghhL/CP1UxR/LOKEqGuhE4ZtNE03rMbgjRURvaeoDHKYRw4K8ess36mLwcOyYDGOLQWV2itZiHkfXNeKPmPzT76QKR2qAfv9wnqcG6LdYOCw+r1QKE6uKTWKtbH9+poZD9gtLtfYovLqprx7/fTyBQDJWZ0K6EYdnZfzTmilBUaqFd4LyOXdYYYZyFcEY9/Y92skC7SEac8U7qgloxVlaqFQle1YJi1CIaZU6LmSOMtqUj3onTBN1bshpFgGe880xgYhkEoaNrivCGNkRwSxisxB8zCokhZTPdsa2mNYkrSRdVaySlTYsBYZufOTK2KsB95+WLD8uiE9dE5/eKE5WqFVU8h3pDHOzSKPGWMlRHd24bj4zPaINbFxjqMhpwDx6sFtzmQU2LKsgQz1rLdB/b7iJ6tR5rG4VpHiBNEgfW80ZAKaT9yt9thNfSLBlxFx8pun9CxzKLCivaeqi3FdTx99iOePv8ErQLXm2u+fvUdd9tbmuroO43GcLzWqEbx4NERP/rpJ5jXNxw//RknT37C8uQxpcAUdpQ8cnP5BuoTbm+u2T1+wMV3X/Ldl3+FZ4+vFTKEMTGWPf/8f/+/4/N/8E94sx1pOks5MN0QoZStPf3qlJfvXhBdpiZFvxBcvvGKVLYMN3fcvn6F1g1qyixbj7Na6NTzDkmEjAqNIo4B5Sw1wWa34eNPPoOXV4SQMFGjjZK40QzOKUrNlJJwnUfnWWnrFHmKEv7TLKm7wM3tRKqgtGW/TaTdQLtY89Q+RXmZBnV/wjevXhD3ga7p0RMYnTCNkWWkslRdiHFEO9jvBo7Xnu32Fu0anG8oYeS7776jP35IvrrFNSvOHn/OcnWEUZZp7Pj2d78lTQOt1ehcWS4d2mS0q2hvMMrgrKGqgs0V33hCTBKHqcVqJs2UTa0sKRXCFEW/AWhdaBuF07KPcc4KIcE2s0vBEU+e/YhxKNze7nCNnBHvLt/x7npkSg3/0T/9L9mkxJfffUO77Gh7J2dCyaRhIKVEMwdsqQrTNKIUdK3HW00sAn/fJ0oq2XNprYkx4Vyl8Q65CApeNWK6mbOEDRlYHR2Ra6XUcu8G4V1Lv6hMU2AcR4z21KrIWRwj1H0zLmrn6uTvIko8/FngdCHg1PuIgnt+/x+rKHwYsQnzUHC/cJ7h8PvltiyXc873C+oP4aYfit7k+80HWQvi/SMH8b0VthaGEmr+//kx1VqhqNnTiHubbmsNzrnZcE5YAVWBs46SymxoKjBTUaKDEOtrMeirB7rrrAMopTBOgZQLbdeDiwRG0MIyCWMgpghV0XUGbaxAZt5REFM648QDJqYyc5KFwhpjwjYNKIO2ClPF3trOjIspTCgkHMgbxRQnoTrmyrDfsrm5xvkF69WC1UJxdNSwq0As6AopZCoamwr7fSDmwsn5GX3fM8WJ/X7P2dkxShXZKyQp6bkUSqnE+AG9MAd8llH/8C7WIu9BSnNEqdd0/YLVsme8vEU3iZIncpbC621FWcPR8Smf/PinLE/OUXVkeX5Gf3LGxeU7tts9i5RwNXG+blgDj84arJl48OAI23q6psFbUZ+mMPHm+y/45svf8pPPP+f5w2PC8YrnT85YLxVvXvwNHQmVMv1ouNlmLr77Ld+9foFerPjffPR/JLqeXCzONbRNS2sbnnz8GXc3L3F1i24auuWS6JcMuwtieMe0D4QRSjZkLKVkQjLkmKlalLxZKUJKdMXOls6VcUpcXY+Ydi9q81pp+gXTNMreS0OJGes1/WwHnTTElCilYryjaMOQK6pdUSZDmDIhKvZBMZXMJ08eoNs11bUsFwvOHz/mxVe/pdwFskv4XFi1Lb1tUD7x8aNnfP/6ks3ujq5r6PqO3TiQlBKDy5xou46iG8Ypsjpd0K9OUW7F7b5idSFmaVwa72hUwRSDMgXnNMbJAU4txCLPoyqDcQtUCfN5IXqKVAo5i3g1psIY40zalGLpnBOG4yTIhe86RiyPzs8wiwUpweb6HXkMgGa7uaMMibeXe5Je8s2333Ly4Ck1v6JxRzjT0PiWWsUR9SDENc5Si9hnGO8xWot5YEWa0Pm/+70qHxBxtGTGpDgCmZwjucjS2BrB/dvGM05iy2+Mx7QOpepsE1Pn8DE58/4wskBrCeRR8/l1uN+ShRFVqxQXCRH6YRTBH6UofJhvIB2+HPz3mQgz9fH9v5fZrOkwXpX5+/VMk3r/BHOSTv5w+PMBNCUb9nxfSa0yOO3IuRBzoioRsxn0oVJRUVRVqCrPj2veXSDL7kPegdBWK8zso1qyBM9oKUwUkS6IuqyC1qyOT3DVoUtDaCphZ9lvd8QpSuHIk0j4rafpepq2F8aUsmgrTIqYAyqreRqAlBMlKpSWQ2N+GjN1bTaji4WkNMpCHgcUlWXTEksm7q6Jwx3b60TjMg/OFjx7eooqkWE3cnszsrnLhBjJKDCWxXKF0pW7qzsePnpIzRlUpWkFJy05M4XCw0en5JC5ub4jhEIpkFKm7SQDl1IpUYmzJEo0JNGgoggFTbOCOJBrRjgmopVQRNq2YXG8JlCpMeAby8OHj7HWc/vqS+I4YsOOTk8svKLTd1xdfMlUjljaExqjiPst333/NY0PmHKLzbfU/QUPHp2zbypjr/jok4847gt6uiVut+iquN1E3r75DUM1/OgXv6Qt71DqmGg6tPGzutXw8NnHnJ/9b2n0nhK2pBB58/INd29fsbLQeoWpUIrmbh9QCBMozVrLrGFIAT1GjEn41pJi5e4uMoxii20bT2TC+Q47NaT9XqbXWum8YbloCWFAF+G2VzSubQlZkTEk1VG6liEPDFMhW8duGKl+zSZoklHYRmPaNaY95ou/uOTThw0Pjxr2wwAukYvi4votm2HDlBNlN+G7FSEGjDc450X7EqvQIpOh749I1TONgF1QULKTcYZF03LkHHnMOFtIeaJURa4SkVtKwfmejGU/Vnb7wt1OPu8xJaGhG4VvnSj9vaLVVRoLVdE2zV4+Fq3E5vvZ4+f41QlvbjaSaDdOnC0XXO1v2e42fPT5M/TimG9fbfjLX/+ajz5OfPbpz2i6nvVpz27YEmIWN4IiOwvFvDPNVQSFVXRJVI1uDCXXGS5X1CIxts6KADfHQIqJcRrJOWCtxVmBhZtGmEIpFnKqNL7Du44YhTJdqxSGnOWgXy6XcxOsBOZUP9zxppQQdbOZWUdyzmldUCpIOa1/5KJwOOzvaajzYQwztHRYOsyHWkHUzHru/FNKGGvf6xTuRXCKfxd79gA5yZOdISgtvUIpcyWuh31FRTmp4qUyRxHqOXHqfTZ0KhmjE9a5OR9YoCFpeaWQ1VzuLwqFaCswFesMy+WS1jWo7ChR4RhptFww034k6UTOVWAjV6hKwnjQljLnOVQEIhAvqLm7MBWUxnhPCoLBliyTSmUuSBVSCkz7gVKi6DjINM7SNpYpJkoVC+Wz4yVdAzlWjo9PWCwHrq9HxlFx9uCMmAvfv/wW79xcdCIxBPq+w3vL9u6O3W7CNZrjkxOG3cT1zZZKmVPQikBqiC6kljwLbjRojbaekCq//NM/wbcr/pv/5l8yTglKoWsdRlKDII9YFdntt3z9u9/w7OkTlutjVm2HWR+xvfuWxlRaVTC2wnSBIdA3LU/OlzRkLm4vuXz7LX0b6NzIokncXn7HVdeyfviYfdhQy57PfvSM7buCOWq4uniH0tAsepLxNGaDmt6hfab1p6A8KVmmaklVxJJ9v6DtW7mW2jXG3BGuNvhWY52mJMN+GkghCcRXxPBNCAIKtBbVeLWkjNhPKEOu0C56qrOi7m9bxs2eomHZe5TWjNOA1ZIAaK2iVAPW0y3XNItjmvVT/OKMV2/e8bdffMXVzRa3XNCsztlHidbcjondmPns85/z6m//R0oNaNtAnRijKM/vdjcobdhsI4+e9OQcWa1XM4wqpIxhHLHO4XBQDd4v2e0VKRq8FWub80fn/OKTh7z88i85Xq/ZbS5JRaJRa5Yuu+s8i+Wau03i4vKO7Tay24stSy4a00gXjTegC6ZIcfEVCcOaD5mm9cSp0qDQdWLcXTFub/nsx58zmSXfv9mwaB0hSArbj3/6cz7+SccUPaU6Hpw/wTUt+7jjzZs3OOc4Wh0xDgOLfkHbtSgy3oplj1GyC6lVWD9ai7pazXtLYw651FkyZXLGaDVboR8MMoXsYYyfKa0dxkjhddZSSdze3gLQdR1KKdq25UDM+RCOL+VA8DnsFezs6hzu788Y2UvM4TV/3KLwPiHtwHH/0E77wAiSpWrJ+T6ZmJklRJHW2xjzA7bS4YX8cKcg9aUKjja/2My7BYOe2Ub5B7BWzuV+WjmkbJl50Z1SYrffU0phtVrR9J2oevX75Ys6LEZm5XGtal52V5qmgeSJccSgQDswnqbtadqANjf3cJlv3CxLTxAyrllQqsIJWYTGdXR9RwyRYRxBZfS8dMxlP3cLswV4LfOyWwqX1gpnLZRMzRPoRM0GolwWrXGoXAhjYLGweK9lWexXvPx+w5u332Ftw3q9RCkxpdttNzw4PePy8gJFxTezlW+Bb1++JsdCVdJtFiUXoHQiUjjFEDYJDdYbhkHyq1XVfPPVC8I4YbQSDn7NqByxRpH2t9ThkqZkwu1LfvvuG371p/8xZ+ePKdZyHQKqUTgNWhXyuCWniFU9N2+/Zbf3rB98xIOzIy4vfs/JquHZ0zPGu4Fhd8W6LNhvvqP3gfPTIx6vnvH62684PmkZp4CZMkmD6yvTeMlYJ7ojjTIO7TqssWgNu+0dJ6s1aCXBLF3H8vQhd7tX5Glk0S/JsaJuJkoSx0oyc3a8MM+UtSQquzGRs7icpoKEzBtDjqKTafoFttsRUyQpTcjC8e8bSyqSEKbxLE5O+eTzX+EWp6zOPmZ18pRhinz+y1f8f/7nP+fb717R9Esu3r3D+xZrFa/fXrB2cHx0hsu3s1eYTG41JWEpaXjyZMF63ZHiwG6/Y3m0IufCdrOFammcx7VHXF5cs354TNMcE4Nw4dt+SRl7vnv9Hevjjt4mSvGSqaIV2mpyyWinySqzGfdsxknS96yTJXNBslecpRiFdQVneowaZdflPSlWatE43TJsAyUX9nevUb7Fl4nO7GmWhTF1nDTn3IRKzoamXWB1z9otGPaRzd0bzs4kb2HRt1xf3ZBjlr3gFOj7BcbPQU2qkEIALN1iyWKxQKlDw6sJYULPkNF9DLECbTSmGEKIxJTnKcHOwrQs+1Hr5rRHyxSm+8P9wzjjA6QUZtsdPe/ARAcm35NSmk1AZ+1WQaAq3heQP1pR0FrNIjA9b7t/KKO21kpYNu8xr8PBL1VQvLz/XRqHw59/4LjK+6lEnFclf8EcJNxV3ogPF9GH3/8hzJVimYtCxhiH9xqtFXEasFqcSUuRfUNKQcR2B/FKzjgjrI+maUiTZb9LGCXiOO86TInCMMoV56ywiooIvooS2XpIO6GjNg5jnMQ2TpH9fiDFRIiFNO5BtxK4UgJKZUqR0G6tRb7uvMG7BkMSL5YciUHiF1Oaje+mQussfS/6jbwwtJ2maQ0PHjR89+01zz76FN/0/O6L30KtjPsdb+Ps75TFalthqdowxok0SecLama9iK7DzvBRrgXKPFXliiKwu7vh//nf/2umADUmasxYxLrZlIKphfHuLb/5H//vpDThpj2tbdm9+RJbI4uu4/jkhHB3Q21Eqa0NlJzQac/b77/i4589Z9F7+q4jHR3PYzIcn6zoXMO4+Z4jt2EYb9jdjOg4sdm8xVnLP/0X/yl/87svubjbszw9ZZo2hBQwdkGzPCaniZALz58+JG1ekIZb2mUL2pJCYEyaB89/zsuvf8/dEClTZhgiJWv6rseUQCkTqoAzBmUNGC1L03myrVHRLhZcbfZY76kYtGp4YByXl1cMaaQYcFVynJV2TKkSUdzuA1M15GIYLm958faO0/NHfPTJJ2jfwb/5n0k5s91uaT95DrXy7dcv+PaLP+dxO3LaS8yo0QbfWna7yBQTxipaK26srV9yt7ulpMJ+N0pMadux2waKkQwKhRxsVE9OBZR05cZkUg3EPFGUxIlmpfBdxxQm9mHianfBNlSmCkkpTOOIYbaXtp6pCH0drVFW0zQLsqq4foEr0PmO23fXeC9WH94Uitrh+8Lt1e+oSrIe+tMzTpsz6B7w6uUFTz7+lGm45S///H/i3dtXPH78hF/9w/+Mz3/0Cb8eBrbbDbVdQIXb21u6rhUWmNWUnPGtmGoKUlJx3qIUuFm7UEoizKFBzKeZMRprjUB/ToRoOeUPTDxlgijlsAfI92ebtYdlcxZTUqXJqVK1wLnGCHyVcyEEyYS4Z4DqWUeWxeb/j1oUJAJRojKNkc3yocs/HOLvi4CeYQkJ2pFKdpgsDgviAnPH+WFBqFU42n84KchC+aBoLPcTQC3yPdrM0Z3S75OrVEZ94G57h71/c4TOGmOYJwqBdPRcgGrOGGfk0DuElxiN9R7jPClMsmAyDdv9xN1mJ6wCrTFakXKk5IouQklLU2Q3RWxZsFz0jCFiskBdIWV5rmhSLhgvUEM5ZL2KqY3oTuaM3qdPnnB7845pqGhdZXkc5Nq6vopQr3nwoCHEwjBmVkcdzhYWneFoZbl48z13m0TrpViPux1ptjiPqXK3GcWds4pwJydR07aNKLCZ2RKAqIwrKAsxiQBQabBNRaWMQeNm1aoCYWmZiifj6kTZvqU1mdZBJnH16vcUZVh+/CPaxZI4LpjqKOryXNBKkWLl8eNHrI6W7PY7zh88RJtAHt9i2w6TA9N4TQwXqHpH2O15d1WoITANA9lZvv7i19SiMKbincF7hzEdrWsxxmK7Hl0UV5d3XL5+wUZtiKfHnD16htOZs/MHnC0tKWa+/f0XhEEWxG27olbB24dyIxkXXoSLSkvkpFYGHQOLtsP6hpBusU3LyckDdps7nO8J1fD21XeUoijGkEqep0ZL0prpesd3ry55/NExWU98++otRRlykWn7008/4YsvvuD09JT9dseDsyPMoqdtWqawwR4vGKZb1n1HiJGi5Pdr5VBGtBp6xvCtadnt7tCmI2XLflKMZeLBeonzHXmbQM/EkKYho9nt92i/BRsJKaKtpBIa52fvIqEzZ5UleLCAMuIKvJkmWqOwXlGrhzlwKitN0zUoWk5OT+iblu0uMd2JcWGJAa2gtZoQN9B4uq4hVVgtjthXy/cvfs92tyWlgZvLb9B5z/6uMGwvUNqx6huM1jRNR9st2O9HYgoslr3kOs+2/0rVe12U1sLyORy6WmkUlRwjlSqIAVXEsq2EjGktFiAVDTN9NGfJ24gxEmOYv++H9H9xQBDaac4HJ4fD7lV2DtZ4ijowJiV7O8REin/koiBisEP3fmASfRC8U8r9uPOhP9K9F9IHk8P7iaDKwf3B1CBPXFH5IduJufTM34CEbcpJUz+gwMrvPxQp+buZOchaq3vLbT3DMcMwAJW2a+Y31HBwMJV7EA1DzQXnG9p+yTaNhJSoIbDbjbPlt6HEUQJxrEePiXCIE/UzNUsLHDSFCefsPW0WpVCZ2XtI1JlkeSLKKnmXjAKnUbbi+ha998T9FucN62MngrlRIDzveqzu0ARqHkljxvpEiZVHZ8e8fbclNyKYclphnCWnQhwjYyjsdpn9NHvoKJHdawUY8V7SWqOdpqaAcZocxC3VWWicTBsliq6hzsFCs8WTyEeqwEFWZRpdOO4tpkTqnLqS4g6Molsfw3RC3u8IVZFDpO2P0f6Ibay0KaFcpXc9uXgu3m3o9I7ejtRwC+kdOm+pu4mSKroabMnUWHj5zVf0x2diNHh7w/HRI5r1Q0pzTKIhhoK3mq7R5PGOYXrHJm85XvYs7Bpl2pm37ucPfhbcPBdhy4wj1niU9+R5wRxiRmuH0p5qFEfHZ2hrCSmCsyxXa/quZxgiSXm87zAqcXvzjv3mDmMtuSpCNpycP+HBo4+IqTCVgfXxivXRmpgiIWXOH5zz4sUL/vY3vyE9e8qDs2Oc8yyPjrj9/nu2wdNby+VmR+c6dNMwDhO5ODq7YsgaR6XQcHc7UWpLoeVuU0g03I2FboQmyBSunHzYYoYhZNJ2y+IkU5V4l0lErRARqpGMgKQrZZhQrooBnG1QKTHlzLQbUKPCjiOudZycHfPxsx/hO88wjeRmQbSe9UPDu+kF47CnWxxJvkkJ2G4B/RK/PAG7omBYtA3Pnz3kq69/R047vBoxTcaWPbvbS45OHuEMHK16mm6Jbzqcd+z3A94LA8xYg51N6bQW+j2qkqIkPGplZgRjtvlB9pPT3Ei2nZbJRkGlzM2sng91IeRYKyy290009xC61mY+d/O8M5AJQYrSgfUkVru1iLFgSomc/gN4H70fhT48oGWhC7P6uMyhGVVcVM3s8Ii8bu9pqVo42zLWCF79h4E9ot3R98XoMDHIgrpy8PCRwiHGT+8LiHoPSd0vV2bdRCniOhkz1hn2uw0hTKzyarZg0nR9j5v9ywuZpjFkKtY4lss1Whd2mwumXWGYIiGm+wJUSwalMCpjFUI/URbVOFCZ280VKWc6vFBnnZWJIBUqiUpC2dkkq0omcZx3MsUYlDd88/I1ukZc17HoGsqist9dY63m9iYyjZUYLc4pzs9OSPGOu+sNFIOuhc5YzMIJ3TRM5CkQYyEVxW6oBAl2QxktE48T2mlIBV+r5HUrSFUW86WC84q21fRek0JiirIYKhVKkbzkUuXg1AVR6zaetgFj8mwBnkAXmt5TrMX1K+LyiCnckNDE0mDMI9ann7BRC6Jrsb5lSHB0/AiX9uze/ob9zVtsucCWDbpG2qoR7zJNFT9bQk6EmysU4jq7/ukS1ZyzqSvaxSOUW1KmLY13OJ3JaSBuBzaXrzh/tmaYBr57+y1Xr78hhy21DDPMlzk6PuHi6gajDWVKHLcNpYpBncIx7CPaNhydnnN1c4trWiqKi8srHpyeo4ym7S1nD57QesPL71/w6uV3MyZdOXvwhKcffcZ2O5BKIBl48OSZWEOo2TvJWP75f/FfMAwD7169In7+I4yC9fEpm8uO7y+uefbgCFNahl3Emo6kJVxID5pmsUCRaXqhulbTcHEbWZ89Ybl+SN4ERtURaaRBmC3ra9Fo7UhZArWaVjEOYv8cckYR0M7QdT3ZFNxoUFaTkqYUJz5TBbGx9wavHO36mB//9B9xdHLGGCZ5rdsjtPOcnzQ03SN+/+u/pJTIyelDGpXZpsR+1IwKnEvYcMv6xPDJR+fk8I4vfvuCZddCVmw3W15/+w3HZ0/xFs4fPuDqZsNue8fDx0+xfoPzjhwDudT5kN+D8mIpMR9wggSVeyTksFQuRfYAaha2xejmsy7jGwWqElNEa0PTeEySnWlK6d5LSWux+E5RLMLv96wzEaVoxMV2JvXEkOYoZIG2D2fiH7UofCg+K0XGeKWV2FUrQbDeeyNxr5M4FIIP6VMffv3wsx/eDl2/CC7mr91rB+r9vkHpeaaoGdSc78D7hbgssJmnGGFMyeJb2tU8j3W5ZEIYRQWo3P33j+PIOE6sV5W27WTJZzy9WpHinmwk5bxUdU+njUEmDFXBKiPQTxarh0wW/YFVWC8fJDkhBQbCWpSRPAptBArItVCrwDDVGJTztH2DyhPD9pbb3Q5vLcYrhiHRdMJqqNnw/KNPOT42/Pm//Td4k0mT2A3nYqjaEEIQC++ZBhxjJomBJs45knbomnBeobTQhlMRTv2UknT+GpYrx/FRT+scNQUxE7Q9376+ZbOZ5l2Ok8agZumgtMO3Pb6plLyl8ZYhZjKF1ckJ15stZ+slR6eP2IRbxm1COc/58z/h8Y9/wW2Gu+yZYqaxil53PDp/xuubL3nz6opWX2EoKAOt8Yw5UlPGW0OK4s0zxUirDWoc2V9ecXS2wqgTFutH+O6IzdX37KaJYbeBcYMpmrDbkIc9m5srttev6X3l5Nk57169IQ4juRqOzs5wqxMuby7JZmIPtEBjLTFUQq6cnp0RU+Hy6gbft0zTxKvda3LINN2aoj1TgqZtWB4/YDEE1G7gbLkSaGpKlKqxjWe5WtL1nWSU+JZ+taKiOV6f8C/+q/+af/Xf/bcMU6LxFtO0PP3kM7Y3r9hMe/IYSFNA64L3Hd41NKtzFkdHXLx9QR4jbbdiTBPLkwc8+vhnDNnTMGG7Fa5fU8eBGMYZuJVl7qZUphhoTBZqadOhomVMlZIq+ymyn4Rg4YyjAMM+s9+LSMx5yX6Qa+SYUjpCcISksL6nWz2YOfqao4drFm+35LBl9fic9bLjdgwE1XJxseG3X77g+uaa58+fcHzUsF47Hp8txW4jWFTbYLXi7Zvv6RZrMaxzmv00oo2evYQU1nlijIxhhFjJJeCzv9dYaTOfQarITq5Kp35obN9D6O/jA2KcqOgZCirEyP05d9jXypmmUbzfNWh9eFyGnLi3zy+l3E8FpdR5uaxRf3jI/v+4/d3ZR4K6zH5EP9Qp3LOR5m89QEsCD5V53Kn3XxOWkGQtyDik7u/jHm6Sv0gxqmKOVe+po+JMyIfTxwxF5SgV0jpL10hGwvviMkNISgrGNA5Ya7FWEePEql+z7JZoo8gkrFF4L0li8hDV/VZfaTGUWx+f0jaWuLsm7CqqRkoI8xsii3kzA1EpRxqv6LuGprGkGIh5hCqLbefbOcxcUzBy0c48aKUsqFbG4PWK3d0l1TisNcKMOjJM4x1xEs/73bTh+5ffcX2dUaqy2RQshmFbCCHTdIqmMeIbZSx7AjpVnCtUqzCNJ1aDspKcZZ0mhUAsCZ0RD3sKfeNZHy1YrzqsLtSiMcoyRLBOcX6+IBXNNI7y3hUxbhsn8SJKRsbgQmGXFM3xMdq2DEMlq57iYFItyTjWx+c8ev45/focMOgIu7FSYsQ1Hm8MrfP0ztNqiy6SdXD4UEKmpDK70M6juZ4wxXLxzdcc/Rhs45liYiw79uPIOAW55lOS4p0HLl79nrvtBldHTlYtRiluXEO7ctzuFBfbgY9+9BNYLFEmsdtd4htNGgf2w47V6ojThw/49uU7lGkZRhgGWdy/e3vJ6QNPs+xmO3Lw3YrT86esjzOLxZrNdmBKleXqiAePH2O6Fu09xjVkJPjFuZZdSDz/7HP+D/+nNV/89m+hJIbNjn3IDBFSKDx78gk//+Wv+PL3X/PlF1+T9gOPPz1imxzfXw2onFiVwsUm8bNPfkyzfMR2M7KLI4sOsJZiNEXLUZKRQzsXz34/0luDMR6tHdZ7tIJQCyFrdlNA2w5jGlStZHYUDU3fonUDyqG0ZXO34y/+/Nd88pOfc3L2COs6MmuUXRBSJmXN6Uc/w9lMrYG90fQPj3h88ozPftmxPPs1/+pf/t+42dwRQuD6Ys/xshN776aDophS4G5zzfHZOQ+fPOHnf/qEb1++5fZ6I2eUVjS+IeUtIYxYp8gRxpKEEeiERFLLHEE8K4xLDdK8qkOXLjoW7xtELSXEHX2gzSf5PMQYqbwn7YQgMZwlH0LANDElrGlEe5UKed6xHlCLHxzfH8D3/2u3v7uiOedZmzC7k6YqqWHGiDbgIEarWnjNSrp55/w9XTSlNHN0NWLaNFNQD8wh6ntKqVKS8DSNsrTSwtn1vsE6j55DqNXBYqMIZz6GwNXlJV3X0Tx8KBfqvLVX86illMAi1jqUEl0CVNrGYeddBxrxCupbmULuCbYVckEXRdP0lLoWCwuruAt7xrqhIEXQzMueWsQATxeF95aHx2uUknjS7S5J6lxR5CiB277pmFJB1YLVHqMNVlvxr0+WEBQPHz3nu2kkTHtsqTjXYc2eYiPKBjKF292Adi2nD065rNdcvw2U6cC6Fe/2Q/eCMVQV0RacUnRLy5QLxIwyhlQT2kNOiiGF2evJsdkEvIJ1p6k24xtPUoZh2GJsQtRGFZzoOWJlTpKS8KDWOWKArB1BgVcdxiwlz8KfUUqDP36IagamsuP3v/tLPsqJ9aOn5M2etE+slmtsCWg1cHd9ia6WHDypZB48OCFOgRC3EsmaqvDGK6hy0Kokht0N0+aCUhdssga/Yhp2EixfBO5sHJyuPJd3l5yuerQ+IsdENZ6jh59SesWTk+dgjzl58AS1uuIv/ux/4Gi1RtuOq833LBcnrB+ccBdGlucPOG5OMLaTxWEYePvqG65vNpw1xyxW/Wxc2LE+cmy3e5RpULayXq14/OxjnGsYY4DqUKpFK0/MCuUMU860GJ599mOq8/z+i79lqc+Zrl+z3ydc0/P4s5/xD/7Ff83Dz1/y7dv/Mwvr8etzLjfXvHg34I3icr/jR5//CTSnhNqyPFqxLrAbNgxhT9O1YDzTMFFLYRc11RwzjHfkpYM6CDtOG4pr2I+ZVCzb6El7IR6UUtjsE9o2eCssLaMLtUZKjNyMA7vfVP6T//QhXbNE0ZFTI6QO29OdebyrKCUBTKo/ZbInVL/ks1/9p4zFc/32b1DxFePdBlVHMSDc7YGGmEYWTnN8fip7IGWxTU9mizXm3uq+8Q3b7S1t0XjdYrSm5sQUA8Y6fNth3UxQmdMdCbIBzVXNhAuJWq3KzHoFO0PlhTzbjKMEjZHI3pl0QqHODXk+QLclAuKOUA66p1kfIM15pWTxVPuQfv9HKQo559mg671W4TDaOO+lS/8A7vnQosJaO+NtaS4e6n6aECHahxqI938+6AsEw0uz3kHf83V/oJtAxCN911FPToQlMGdK/0ADcV8tK9aIJ1HXeaDijZ8rrQTeHApgnVWY5EwJI2naUsKOEncoJhqnKa0jeMdknVRs0mxAVfjAEIKubei7blYbOkqJEokZxC7bdkoEMlRZOFMPIXOA5EuUUnFNQ9cvQKUZg+5xzVbyKqzBemh7SQdzrmF9dMTd1RVhyHStxtqKsTK2O+vQqTK7VbwPG1Jy8MeD0HDumFKAmitVWYwTBer17R3LttIvHuKbBReXt0gUIGLXgXhPgfjaxFRJMVKKFiNArUjVoIui61f8/NlnpFy5vrwklcJi0RN2l9TwkjcvNnz7jeX7t9e0ixN+8uOfcpcC3797weW737G0e2raY2R5wSHIxChNTFGYRkXos6LyrOQwsr99x9mDH3EbI9vdHaTAtNsxbPcsioQ31VI5Ojom4RljxS97mn4FK4t/sOTo4Y84e/QZSlsejTt+/dvfM+WJiufs7CHOVW53g0AjfUe2S/qjR+QM3sLRo+fkWggZfCOdNbXga2FhWqYxUNH0fQ8lE6PYh5QMjfU0TU9C03QdR6cLlALrHOfnZ7x5vaLuK8cnj/nd735PU+Gv/vpvUdpx8fYKa1t+/NOfUbXmdhuopic7y+mzTzh9+jmJjqw7lLWcPXyM37Vo7zDO0hlR95ac6U9OyeMTdpcD29hijWaMicvtyPL0BNP17HaBMe65vrolpa0QLuBeYGW0RlE4OB7nVJhuL3n57TdiZrc4glpou4ZhSlRlCCXTNi3dqqPaHtf0xAQVw5/+o3/Mr//shrt3N6jFkjBtSCEwDYbF0QnN6pSjx4/xvuHd5RWXtxMpyRnUtp1YnSMW1m3TEacdwUYs4l/mvSPlRKul0RIkW2jkNlpcciglh//hBFJV4PcyJyWJJkE+c13TUKkMw048k+Z8e4W4DQideDbsFF/VGbefEy3r/HcREYlb8d8RQvr38j46rMI/PIwF+6r3h+4fhu+859rKEz7gZAcWk1hYmLkYvMe+DodQ13X0fU8IgVIkDvMPtQ4gBUEpyS323n9QBJghKnlJ9YeFREmmsXXyc5LlOSuhVb2Htg7irJonatoz7N6xvX5L2L+l1QHfN1gV0SrLKtNa0vzmGaRCq6Ios4eJcRalHbWKMtk7EfONc3504xA7gIMeJOfZGkDe/rvNhlwGTk6OGMc7ckksT5cst3viZcY3LV3rCTFQasNml5iGJOXJgnEa7SBryZMeQmS7n5jmdLVUK0yJrDXM7o2lzJue+2lR0sUWTYuxwq4wCrSyUCrjdsBqLc+9QrVGpsVcRaOkYQwTm21EUYgl0izWfPLZTzBNy5RFHa60YtjvaNqJ3lem3Qt2G+Hqt7HSTNdcv7hm3O3Y37xFlx3agzISEbq5u4NccMYSB7GhSFF0HblWUZvXSEg7vv3dr+mOnqDbB6hkqCkQdreQE9Y6xnFiGDKud9yNGr98CN0S+jUPnj5hYoXpzsntGavVGh+2/OSX/4jvvvw1q3XP07OP+Nvf/lo6ed3hlyc8ev4LbHfCOMmOK0xbmsagcyKFgHVGKNKqcHT+mNcvX5Lutrx48TUnRzc8ffYRrlnh+pUopDM0fYdWmpSiBNbEieWy5/zshLsa+fJiw9t3A48fLVCq4eW3b3j18jXn54/Z7wKr5YqT0yf8w//on/Ddq9ckLLtsadqefVYsFh1ds+D4/BxnFcNuh64K3zQYe0rYdYzbC96++54ud+w3kVdv7nh7NfHxZw95/skjbJtRuytKDVQk/ErrQi4TICSQHKN0ymmihEzMib/9y/+J06MjHj18SC4F71dUJdqfiqXOkFNMGhszvmkkeCvt6RYrOvecPFhefPU33Gzu0HrFRw8e0Zw/QS/XYncdInHcirL6g3OlVGi14cHDR4z7W4Zhz2azvS+8vvH3Z42mkkuddwWZrm1xvsXaBmuk2ZzBI3Fw1h9A8TCjGMzasDnRLaX7szXPjZSce+/3p4dcJnGilj3goVj8XdcK/16K5g9vHx7+kg70viv/dy+VPzDPU/d6jXuMX/3BC1NrRTNntH7goWSMudcwfMhYEn+S/IP7TSlR6mFLP49u+kCFLWglVftgGHXQXxzUw7Xq2fBdkth0zZQ6sr97w9uXX+D1gO0UY3bUGMnTiDeGqoSGqXRBz94pKLnvWCRFbdEuyK2jUsUTXkMqgRxHqJIPG+ZOW5gMhZArdUy4laOUIiIpYygpsRu2THHidjtgbMPZ4ohx3DGMhTDuGHdiU+E7DQ6yVgyDGO1NU2aMlarmFKkCVcklW0tFW40IyhVVV/F0SeV+Se21ohrDMEVefPuKlNS80FWkHLDWSDOBKM2VBmUqIUVCznivQFtss6A/PqPaht0UabxjuVowXFuG3YD1E8tGrA3GEJhcQekdagz0VHwHFE1J05xXLDbuOcFmGCV3qjA3BsKQq0rWoypHthff8u0X/5b25Dl0J1hraFXEm0oKEd8Y9sPEstEs1w9pTz/l6PQB3fqU6tbo0mK7c6pqGYtGq5ZPf/wL0rDl8vXvePfya8Zxj+87FqsTPvvprzh5/GPuRoXLYp9+e3NFKRPDdDMzpYR1ZxXEoinKYFzD9uKCu+tLlouOo0dr9MyMK1WRckY1ipIzsST225Hzs2OePH1E7yz8yT9kuVzx7s03GKW4u91Sc6FznsY2KDQ3txu+e/mG292IbRZ0ywUhV2xr0M5LFohzWGc49mLBEEMgk5mmBpoVbv2Y6B2v3w58c5HZjorl4HhmzzAUbjcX3NxOGFNn8adQx50xdI1jKglVpXgbKipO+C7xxV//W1588w2PPnrOk08/5cnz5xQ8MRdCEiRBacvB4iGmwGa7AWW4utmS9jv2QyXhWPRrdLOgKs80FXSTMVFcBPTMKw3TyHq5gKqoKdC0LX3rePPmNeNwjdZi8Hje94jeIM97AHEsKEXhXSt2MDmhrMdasZ3QWtxOlXkvYjs01AcK6YdnK8z7AyVngsR46vmMOFhpvBf9fuhW/Xe9/f81KXzYqf9gHFIH2tNhgpCO8MON+YcaBgBrDR9u2T+EeKy1gsXNUNTBKfBDpfPhsVljqIfHc9hvaLGnVh+82HXOZxA3VhHBUescmD2roQ8V9wBXIdgeecLUQKMjq77iKDiVqHEiDBM5TzhriSFy4AvrOcntQM2NIRBTYtG1GONpGvG3ibGQkvjKmxrBSDJTqrMp37z3mEJgs52wuuXV61coBd57KhnXWqpWXF5vZPSNCUWGPOFMpfMO5+T1GWIkBMGlCqAtVCT4xhZQxoGaBWkH/yk7+9krTa2aEAutVaQiSudcYAoRq8SqoKZIY42wM5DnUVUl5ELVcr1kpcnaUG3LPsLNNlCbiW59QtP31BBYLI8YwhtydmTrSEXTNg1dWwnDxDQMqKzxVaNNR64STmOUYne7JacibqozI6Mm8SXSVijEKWdK0cTta66++2vWcWRx9hHVefLwFl0GagnU6ri6vKKaJZ99+g9YPPgE7RqU79imim1alLFo59EVxkGCYR4/ecxXb78i5MJ2GBlu7nj47DNWiwVPP3rKI92x2Qy8fvOGmzTiTGHZGa6ur4kTrJdrKor9GDg9f8j15YW4XhrNN9++4Oenz2i1oe97fNOy2e8pJeGdJ+fIbntLGDY0jcc3DWcPH1NL5uio4+biJa+/fwGzZcly0dEtl6xO1ry7vODu6oKmGzk/ORO6sC2oGrCuxzmPcXbG1QteWy6uLtjvE/36AY+0RanCE39GaR/z17/5HZd3haxaYp64udsRk3wuQpgNM53An845wiC7HHGsUTS9gzox3F3w5t1b3ly85sWrb/mnzX/J6YPH82LXEJPBtw1aGfb7PdvNHdOw5/tXb3j78g0635KyQzUNy5PHZNNQsiLVysI0kumg8tzMFIb9nm3T0HQLSSrU0mAuVmumGNjtt0xT4vrqlqZt8LPCOUZZDivtybMvmjF53m1qrGugiu2JVVYstUX4ICyinO4ZnwdjUDmH5Qw9kFkKFVW4D+Ep5UDi0fckIKj3Z9AfrSh8eLtfZjDvFqqMOQc7igMWD//L6nSYCqQKyn7BSHwR703vpP4cOmz5H4FxcinYWU7+YVE4FKkP4an3dhsHBhQwi9qkGCViFAhHbLTLLDw5TCDi6Cqh7oU4jYThBlVG1guPrgFypjFeQj62e7SRiEtTClboUcJeQXz6S84M2x3egFKZEOJ9XoMxClUqlIhShvu4onvmUxb2Rgjk4pimjDOKFAPDDDUcHzdcvBu4vrnFaocGuqZF2woOfG8JcUIrhzMCn5ErJQvDSRmDqVpsGerMMbMSNZmTdJ4pSjeilGa7GzB4ei86jFIyaFn4OyPLs5KC5BNgwGTyBFUrXGOoRlOdp5qWYluU78jGUrVmKpmaMmMqDAFihat9wbvKotV4XUj7QImJcTuIt773oBQxVkqSSFCjNGMopElw1cMsefCmoRSMqbR6pAyvGW80Rgf65QoVr2lcxDlDyVGmq+sL3rx8wWN/hOuX7G8uGIrDrSYWJ2INPYXId1//ju3V9xx1CmUNru3pS2G7f8ff/PrXHJ895OjBQ2y7wBfQ+5f46S13d2/pesexU9xtRmgU2jSEmBmSYrE64vzhIz7/0cfs9jt2+x0PWy8Lz3EvtGCtKHGma1N59/YNq9WSvl3QLheMKbLZbXGN5/zBKSUE2lYTpi3eg/aGvikcd4UwvOO7397y6Y9/gc4ZrzReOcDLNekN1Uj+xrJdMtzc0bVryNJIuP4Uv3zA2xvxKIops9ttGMY9VVeBU5ljJnNhHCJpGsgxYJHFrLNGIBkSShdOjxaUxmE1XF9d0vRLmu6Iw4G63+9IacI6x5u3L/n+2695/f032FrwpsH3YinTHj1ENSuGqbALO5rlRO+WsoMylponQpi4vr5iXTWro2MO0vyj41O6fsFmc8s4Duz3O6YxzMiG/Lz3knanlMHahloVKSf0nBJXSqHMFv4e8E0DM7SOUvc+cTHGDyQBlTDF2Q77vW8cQJ4/n+Dm+53RGw7uEv/rt3/PkJ33I87h9qF+4MPv+xDWOci0DwlFBzWzfD/vgTDKnOCm7mlV9/d7HyBh73/3YQIppTCNI8652U2Q+/urNZPSXKxm/yTFe9+mGMXC2ViJqBzH4d4KwxpLigmVxSteK1mOKqVoGg+5oSD5CjUrqrqlaoX1AguVKvbesoeZDQNzYrvd4pwEeNciHGMx0TK4WUKvVcXNj1VXKOj7rkCgmcSicbTeoL1m2GwwSnFy3BOmxPYuoo3FW0+/6CRAvsk0C0cZFdXOSlSTqSGBUSgledVKV1mUF7kwVRWzvTJPino+VlMu1FSJsYiwzRi0keJnvRELbl3nnA2hFDstC9VqxDpDW0e1DU1/RNef4puG1fqYKVeGcYejkNBk3UCFCQglMewGVNgI6yglyIWaYYoCz6EqmiLmakgueGaO0Zh3cAcyRi4VbRXeJqZ8R9gZUp5I09EcxRooOUjToxXDsOHL3/4F7y6vMK5hFxKhetqTZzx6/nOefPRTrm82jJtLpv0dt1NmSoWPPvmcNA7c3tyhS+bP/of/B2+++4rFasmPf/wjrt69pTWGxo+UnLB+QdYTOhi0X0tgk/G0bU+/OuLlm3d88uknjElRc2Aad1BFVOiblhgmlFKkaZKcb2O4vb3j4ckZDx8/oOYNJW5ZLRx5GvDOoFRgmm7IQ+D162+xcaJNif2QcfGcbqkgaVQxmOqps3dPmTFvbwy6FGIYqTljG0eula5rBTIxInQcxg0p72m8+FqRhG5ZZ9dR5hSzWqH1vbihxpGqzL3VR9u0PH/+HO8ctzfXPOpWOCvTKkXM68ZJ0hibxnF0fMxR/4DTo46mEWW59j1td4SfIuVuYJwy7AOViFYKqyqr9UrYjG2D856U09y9J6zzUhzGEa0Nw35HzpWmcWh9gNLFZ8p5A9XOYjLRT6l5hE4piUC4FPJ8PineRweklObAMPk8ydl3sBPKOOc45N4bXbGz11wpEg6W573k3+X275XRjFY/gIruGUjqhwvmQyH4sILdL5fnw79yKAj6fsK4/30z1nb4++Fn32NrPzTdy1n8Qj6cXu4VfzObqMwcYGFC6fvxyzlL33egKtM0kmOAItnDSknU5BAmWqfRJYpTpXYYu5iFXgZsj2k1rl+K26TSGCy6JHQWEz9rFDEqUixMw8gtwpywTstSOYuxlTxHQM2W4KVglINDPoXWOCcdYE6JdtXTOE8eNuQ44p3nwfmKabgh50iIit2oOTkSXvYYI67zmAoxJGwqGJdEuJbE96nUjEKKU01z/nYRCrIqB2hNqMAlwzBEOg++dwhtK9E2Xpbzihl2lF8haltFUtC2ju5oTTYSUZpLYXNzSb8+w5DxXpNDpGk77NE5R6sOf7SAOnHz9gXTVjHeXszUvcPj5F5TU7XshCRTep4cUWK0UwqZeJ+p0WiNQogCOd4xTHum8QaolDgI3GQONu+RmjbcvP5C9lxNR8RhamDsPLdW45s1pkZ+98UX/Pizj3n+2c9ZrxbkYcfjJx+z31yh6sibb39D1yruXv0ZjYHOWhZLj7JwNcF2cpjuAWdPfzZbumf65Yo3795Sa0I7Sw0TL7/9PavVCmMbjk9OSOOO5XLFbr/n++++5fLymqZp+NGnn1JInJwdY80z3rz8hrdXb5h2Nzw8PUZXMLUy7i5Jm+8xOWKoHDUtu8vfcnRsiHEibIXm7dojNJaYIzGM2BhoXWaMgcW6AVVlSbvds1o4PvroI7yHm5t3qCqZ7GHcY5RAejVD2zhJMaRyfnpC4xxt29Ave16/u+Rul1Cq5aPnn3B29gBlHdM0cH3xmvMHj7Eqkyjzgjmy6C2ffvqU8cGKVS9Ctf2wJ2PQrifpluX6hOWxoSpLVmKGOQ57ll2DdxbvHb5pUFrhjMByxhhZ+CqFb3tOrWffbRn2W4Z9wFpR7VtrD3pZvHM0qwUpldnptYKq83WYGcdh/qjJhDfsdwKPa40yVrK0c8EqQ9O05JIZpkmEmG1L6z049b5QV0FbBAExf9yiIAteJfFxHziSvl8ev58gDoXhcABLZ3b4pMrBcPhareUHBzxzsShVovbef/19wVH8cNFsrWW1Xs9d9mE60RysMITlJNkEWquZHis7ClFEF6ZpJEyTKI/DyNXtFTFNHB+tMRScacXoyjZAK9VaVbRrsW2PcT2rIbK5vaaUhHEaX50s+xBOvHWGCIwxst9KelrTWNBKmAolCu4YM02rsMZJKpwWm2CrFVrLBUJJhDASJ4PJlc5rEophGlj0S/recPFuwhqxF6wq4Xymr4pOW4n/bAzWG1msK5Hd55xIKYsBHRpFgir0OemsJVEKo8lktFKEqbK5i3gNfqmEcaSFnoiqs/U1pFJR5NntVtE4I8wr79FGUZVChT15d8VieYyxlillmvUK01lWS88+7jg+PmLtn3DzLvFmvCFsA9YqYTkdIEKQZXiWYKWYK8a+x1pLieQiofAoqDFjjDzGWtPcnQVyKRjL/YGlZ7lKinuhgbYOQ6YmDWMH4zWNSpyeHlOr5uz8GUl19KcP2O93DPvI809+zHdf/w3TcMHRquXk2LO7e4M3UGMg7S0hR4ZkGGLPuluz6hv2yVCMI88Ri7/45c/Yb3e8fvWKUgrX3nN6do4m0bQ90cku5+c//RkhZb755gX9YsE47NGm0HQtj54+4/z0mN3tBWl/R6MC0+4CU7Y8WFvSZseyaxnSnv3mFZdvG+zyMbbXtIsTSvTyOaOyaKESCdM1Wkda12K9Y0oZlp5/+Pd/xTCMfP3VV+SSMdahShZjQqdnfD1xcnKGItM1ntPjNZcXF/i24/ThKS+vrklK8fDJRzx49BQJpK/0bcP29oLb6zecnZ3jXEtF451njMI6s84QosCRY5CkO9euydVgbCNZzM6TayXEQI4aYzUxBNgrmm4p0/V94pqm1ihBX1VjTRbIyHn2uw37Ycc0RXwj+S3jGOjawmK5xjcN0ygOy2LsCcWW2c1UEWO4PxeVmsNzdL0nzejDAV8rMQViEBjcWmkgP3SoVrNe6nBu/9GKwr3R0jwqHDr4g6f3h4yjw2F/mBh+aI4nNz0vaw4ZCLI9V/+LieH9gvqDXcMsCb9/EtbK1uHAWtIHtpLYSpR536GMFAVjjZTtWig1MwU5AJyRMQ9t8E7N9xOxjeeQt+vbnhwKOWuc6fDW0CwaVBkJIbAbt6Qh4nTFOQO+QRWJUTQKqtLM0zLTmMQ3ad5sC1sizjbjiq7XYt1dxA+vziK9Oms7Si5Mw4DxsGxbTOfJV7egI+u14/IiEFPB+sowBbKqMBaSmjBOsew6Ec5YcFUL+0pDHcNcRBHrgprlvZj5z6XOIsAZTjoE05Vacd5jbaGS0VqW7HY+qC1iP1ytZUqFMG7Rk6ZvjrA6Y22lxh3bty8gDhStmEZR1ZqaCfs7Urgj3hRy2jDtb3HKoJyjxrnb+mBazUViSJVR8/ORzc5BTGiUQluDUYK3GiVMtMoMkc26HDuzlVLNQmioUvgw4G1BqUSrDbu7S16Mf8snn/9jqIbzh8/5T/7zU7b7Lcom0R4sKgx3PHr2Ed+/uAMzYX1Bu4BvDL7VNA5cUeShsGoaFJkpDlTd0PcdpSo++/yz+fXPdI3hr/7iLykV/uk/++eMu1t22w2LvuejZ8/JGBbLI548fc7tzRWbq7fEcUQh1vDLo2O81dzmyMXL71DhiqM+E8MeZSLDGDCtp5rIdntBazznx49QeSBPmtZKMU7TwMvvfsdXX/0NnTMcLTuO1sdgO3K17Da3XN1sefbsOT/5/Kf8v//1v2J39Y6+7WhbR017qBL52jQKZQL74YqQ79hNiq9fFm6HAduuOT5/iPU9ylhiDhQd6VvDd99+y7S7YH18jnELdsPEdhjo+sUs2DS0vkfplpQMJhuaxYrGObk+qiaXJJ+/rqdvLbtbyZ7wzjNlBJUgi37Bi9VGitJQOWex2tJ3C6ZpzzSNjGE3a7gqw7AnhkDXu/lznihB4Cg1axWocqYZoylVYO0Qwn1Og+LgBMH8OFu8d1RgGPb3thyH/cOHEQd/1KIQo2DUaoZwfnA70J/qIcvg4O4nB708lrloqIOn0Xu/IOB+4rivjLMY5v0OgvufFxhKPMMP8BEw832FRlko81ZbulSlDEabe1tvmHcOJc9L7Yw1c/ZrhdVyQddKdfaNuBam2VtGa2josMZjrKFtlDBUamK/v+MmjOQkOLbRGqu0wA4lo0rCGYUxkKmkIpi2GMeBc5WYIylXSWJqPTEVed2rvI7UmdZZKtM44ZUBZ+hXHevlxGYXWK8azs4DFxdZSAAFUqrsagSrsbUyKU3TyGttKEQS3mm0lqVmnt+3mkUBXOailJOIiYy1kAur3tM14G2d6YStOMY6i64ZkjxmYyS/AmMxtjLEwPbmkv3uDrA0zRp0S7NYs99cgXGy68iFznuGaUed7oiuYEzEakXcSmdVk0JXWUseRI9hKvfTTFWyF5lCQptK23gxclPSBNg5uMi6ijKeVBTaSzNQ5mvbqILGkRNMKZFyJIUw0yYtNVvaRYs1nnFKZBSL4ycsHxhivCHlwlgU+/2exfqUjz79EW9f/5rLu1t5nZxMcKkILLgvCdv0RGUknMoalDGsFyuWywVvXr0QrF4d85OffMbJyTHrRYNxCuMawrSl6xuUX0DVLPsF7968ZhpHUg7ENGFUZU+hUYVhGNlvtzRlIJSJHPZUKkkpCcWpiZQG8u6WJ6bS2kpRgWlzyZu3r3l38ZKiRrxPKCZiiLx7u8W1a0Iy3FxtCVGx3e5x1nNydMr23QWqaMiVtmk5PjqiMlKRDjyrSr9UDOMNd9sR5ZacP3rCan1MKQpttCx4py1GBygjF2/f8ObVS67vAkPMHJ+d8eDRE7zvcLrBmsPpoeddXWUYg+DyShxbvfd0jaVrLDVlSj0QairGGnKW5a9WmqbppQGsEjFwsK9wXopGm/zcJFdSrEzTRCnC9jucPaXKHhE0yso5NgVpNA9npFjyCBR1yIaOMVCZsxdQ99n2hzPxsJM9ZNf/UYtCSEFggPKeenrPQprpm5SDvQWYOW+hzCwjeWLvBRSSzlZmqKjeP2CZAMQeth4KAfNhiJzzqcRZs/BDuKhUSLNjqVZiUGWdxRg3/6yY16UpAoWconSzsxowpsiiW4KSYpCKpdZZg2EtahaDKGdoWy9FQWuMSajqaWpiebJj2A2E7a38rAajKt4AJRGVMIjQWhxDleDdqUCZ3/wUxQd93yfWjTipVrSA40qJTwR1psZpctYMk3Sb2lqMC5QaOD727DY7tIWQCjFpSjaUbWHRO3RVOGOoNeGtxhgvkEsQ11KrLAVDCYUUE1pbSj3sj+S9bNuGk6MFrUmUtKfECbyew0DkQ5RrRlfxQlK6AIHGWDENrJVSIiklhqEwRY12d+AMxhmBbYpMWa1XHK0MVVuW/RF319fcvovstwFKRdfZJFHPk+xcPFEVa0F5KAliqXjl0FZEXlBRNWMZcQYwFmssVVmKEh55rZWQIzXD7m7DECZKzSxtQRdwviOoFWcPfsTi6BGTkr3ApAKlGpxxPHrwiK9ubynKkJSj6U84On7CuHuNNUl2WkSMVew3A2OyLBaOk/Nz2q5BtUuqa6jWk0rFL9aoGtFe82n/GSdHC4bNLZpAYxKuKoab72lW52jTM26uKWFgCAO77ZZaEq0xKFOwHqa7S1Qa8I2iKFidP+Tm9k4gCa/RtbAbR4bxitfffcnDhwXnWl588yVv374ipJHFsud0taAWGMMWbTzWHREq0lzVDHlHHjOnp5bdA8ew3eP9iuPVkq5RhDTROEXXiR19DCO6WGzN9KsF5yenGOXIFW5vb3jz7iXGBGraEaaBHBP7IRLHSt8veXR+xmKxIBdDroqExhlNzAkdRmqNc2RqgyqWfrnm6Ggt51yFplvJmVULWhURaKpDuA0oVaRga4vHUoonp8QURgnl8b0YQZaCUoUQIynP1hUHr6KqyCWKxqKKjX7OkZRkd+F9I3C8MjOiIIhKTJGYE86KOZ4qUnxyyuJUbe17Qd3fUdL8d18033fYmYNc+/2imZm+OEMb8wdTUIMiZ9n8oir1QTBPle/V6r224FDR6twVH/YHHAqEOuwhwHvxDaHOIS73NNLDYvt9Ylw9LDvJAmkZMXtrGsNi0d0zi4zz8v26YrKBmmD+GUom5YrRHutXOOvF26QESgLt1qyOHjNuB25DIucd2syHYa2UWGi87GVikt2I0xozTzzypI1ARjWx2eyxrqNfLMmlAgWqLIIlIVngkpQgWs1uTCidJTNCJbrWslxbhkGS40IuaGV48uQZHz0956svv+B2M7BaePLsfIsBjFAJKxrjNUaB0Q1DyMQgjrQpR3StdK1B60TNI6pESlKMeyX0WiMNgrWWQiHOmhNVoVGyD6PO7B9lMNrimoaqLftxQGVFyBPWQMiJfU7o4lg9eUSJibvLPdvriTSBPZAH1GGxJrCQmq8di8LUWbswBi53W7SZOD5ZUVVhGjYcdYXGW4pKDLFQlGZ1dMpPf/4Tfv2b3/L29S1hkjze3RTxrWapJPsiKsemGs7NEoylpInd7R3fvbvk4bPn9OsV1cDHz57w+901+2Fg4VpW60c4XdndTlhVyBSePHnGZj9hosP5huVyiW0bsjUUY4gFbNvRlWOmYUOKgVQj1zev0HGHrhMlWK4ivHz5BVe3E6454uHDZ8Si6BYNw7hn2icRoJHZ31wSdzeoMjGGCTqNWx2xtC2b3XaejArDbsRazfdf/5brN69pm4bN5hpbI5ZK3Qeud9eEOBJL4fFHP6LtPXhPxFFvbjGMxGmHNhtOHnjCeMNqcc5y0bLfXaNMIpeJacg4VXFKEXPCYui9YdF4YkyYpsVYQ6oRapgZPyLOpI7kOnK0XtN3C2qF/ThiXY/2Dt82oktSSfaIORBiEJqqP2HRihVNzBWlCylPkCLaQM1FDmClJOdcyz5QzeabU5hIUdyAUfoDbziZbLIYaqHlSLrfHeQchS1Z5sAc5LN8OO/uz+IPaPsHcZqxTmDNeoDrhQDkXCs7Da3++EUBNOWAyKgDrPMex5eu/uBj9L/86T9cSh8cU6H+IE/6Q2aQTCHvLTOQe50l4Ifw6xnjnavn4d/EFuJgkX2ggImnUN939L1Q9rSW/GVr9T1ldhz374vI/aK7CqcR6QhSrmgzK5ZrnV8bS9OtOT59TBz27GIUZbO1kjVRJReClEhEnFIYLcvUWqHMy9Yi2hliDOx3W3zTYJ2TKScWak0UCvUQig1zmLmIWCoKNesluqZwfSV7h1JhuVxyvF7dv57OOUKUpav3VkRCjcM6xRQztUrEn2k8KQS2+4BzlpgzqhS09vO1UHBe3siYC2F+DsYgVhn6AMHJ1KDLnFCl5D2t2TIOietNYLMr9Es4Pu7JUdp9o1sIA7vbyo0deTfsuLveoYrGaYV3XhZqJcxukcxBKHPlkWUUOUmDEkNltw9M0yXGKpyt3ASwOlARKE8bcPWOV1+94PbtlRR+05DQHJ0/YhgndimgtWIzVMbes3xwztvLV9xdXfLdN18yjIHNi7/F/+nfwzYN68WSn/z4Y37z13/JEAJOW7LyrE4eQb5lv5m4vtmzWC6wznOz2fPmzVuefvoRMUZQCW1mWHY+LJaLhrHC3eUFDXtq2jFOe7b7yHaCXBuMP2bRwdHpE54/+4wnT59xffEOkyJpe8nV9R1j3NG0Mh1f3W0Yq+Ljjx6TS2G73ZPGhDcdJWVC3pDCnr3WaA1tIzYQBTXv5ixVJdq2QSlw3rJYCNyx21xRS0TpwtG6Jxwv0Soy7DbkOLHoLU2r0CpiVcU1hnGION2iSyKOe6rpUD5xe3M9d8QNxmqc1dSs0G7FVG9YrI5xvhUX1w+pmlSUkcnSWE2t5h5mec9me6+Vkq48oAu0bSfXbMmAlTz3ArXOGQYhzDTzPNNWNc4ZlDL3JIZaK13XzQVidl+YkRfnHKVM5CyLd6MFFgWoyPdMU7hfQDdNd5+toGa2xWKxAKBpGpw3HFwR/i63fw+X1IPAavYDIguTiPdSal3rLEKR7k/we/UH/x30A7Mar1TQ3CeRSaTcwX9czwZ45v2SpOr3trSpSEGcKbEi6z+wnkSw8YMtvJEK672f8UMlLJQkk4AsaCBGUVRWrShVCbVy5pS1rcTx5ZwZxpEpCvRgFHjdoE1ldXRO2G9J08j+ZqQq0N7hnBc+Tx3Q5pDBWnBW9iF1LjCxVjnwtSVME5vNLccnp1ijyfEQ4PFBvkQVS29l1BwADpaCRrNaGRZ388LZOEpK/Oavf4P1mr6zYqpVIstFI+pSJYXLOiOLdT/7vhRFVzPNEKg50XUabx1Na1C24IzCa1nipnpYiktyW4kFZzRaW5QWtkjGCDO0GipmXvBlpmlit6u0nWIaJ/qm4/TolGmI3AwZArx9tSFPYn+tsqPmTCxxHuMLTuKQMareW5nUUslRioI3lmwLdJWuceSS0FRhn5QCNQlMoKHs93z7+98zRUWzOGE3wS//3j/m8ac/57/7l/89N3dvqSQGZfmTv/+PefD8GeNuw+3bL7n66i94enbOKhf+h//2/8Ly7JxHT57z05/9irPTJRcXe45Oz0mdpcZryuSI08Q0JYweiCWy3cHtcMHqwY5iLBpP33QzASBhnaE1BuMN+5pI04Y03RDjXpyAtSNOQaa7uOHxoz+RJqxkao3EtAM1cnTckYYjhs1bpkk+RyFGvPcYpTAYnGopKTHu9ihdaPsDN94KFKMarO85Oj7i1avvub6+5eGzxMI6MI6FcxLVqgL73SWUhGsU52dL7i63FKU4OVrgO1itRb28v7sVeGS2twnDltuLN7RHwmp68dXvadcdbbciKwXKCazrFWscTbdG2VaWs0Z0BaJXAtd4QRo0GCzWOoy2jNNEvb1Fa8sYpHtHFS4uhAb89OlHOCciyWkcBJ2YBagH+rZWipozYRxRqpn3Ae9jAoyxNE1LmPOY8+x4amwR36uUpJg6h7ViJ3LISYdKLgmFxjhLb/u5MdPEOfFQFNFC1S813Z8Zf9SioBCx2AHqqflAB5WywIFxlAuogjKWWn+4JH7PTir3v7XWMmsJuF8aH6aKUsqcVlXvl9BSTQV+ykmegTEHNlS6ZzyJPbcSWfp8/9ZYnHWz0MPMvPpWqIc5UYkYI1Gg1oqqtmRN1YaSE8Yo+r7HOcc0Tmz2t4T9AFVYHL23eO2wfsny5CFhGpimHdM+ip9QFiwT5wXDV6ILyDkTstgtaKXv/ZMqmilEbm9ucd7TdZ0URKXRNZNjIiaxyFTG05qWtrVAlhG3OvpOE5Pi6nIry2uDwGSqI0xysYcQURia1qHNQWQ43Sd4uUaofKujHmMNNSsa5yBnathT8iT5wSCdl7HElFFao2boaAgy4VhjcNpQkpYpKytSZW4ONMenLevjLGNGCZSo2N/dMO4TphpZWiMUwBgjpCSvyaykNlqjdLnfaxllqbPm49C+xJhYLXo+Pj1jHPfc3t2gjCNWi3cWpxImjbL8rZWYKqvFGtolx0+ecvbRj/nkH/xn/HN7zr/+7/6vxDzwz/7Jf8b5p59jreHbL3/Nb//Hf8XjTnPmPU3JPFlMfPnVn3H16gscIw8eP2O3tRhtaZZr4hC5216zXJ6i65bry3dMxdIdPadZPZSOUhdyHKl5ZNGv8HZBnHYwbSEEWg2bYc9+d0MukeXqhPX5R3zx5VtKVnTtkpoh18C43VDCnjjdMt6+otMDjx6vubEbLq92nHbHOO8Zwp5x2pOzRquG/d2OWjWuU+QcQBms9QyTouuWPHryGcfHa95dbdgOb9lsA499z5gUxioWneN48ZCL1wMXb6/IMc9d8Z7las1i4XGdousbSqwMQA4jxjaEIYCBNNwx6ksmNjijKDFBNfhmiVKOxi9leuiP8K7BNR0hF9pW0XYL+tUS7z2r1ZqUk0TyVtnlKRJaTeSkcNZTgJQCMQVub29IObBcLTk/e0TTNBxyklNJ760o5uPNe8k7qbPNsVKSydDNrKV7gsyMjEzTfmZJyvnVth1aKyQhMch1ryU5UArMQZArP2/mMzfFfP/7hY4vE8cfvShYK/GNh8P70PXnnMVDTquZDSTd5h9qFz6kQ4n5ppnHpEKeX0Wxk/4htbUUyXk9FBZhl9QZNiozhU2e/AHaSkliC62x8/0ZUoiYxsxTxHuDPGMN3jTkLGNzqUJHAzEWSyHKYyhFVMxotDL4pqGnQ+nMuB8ZhomaK2bRomvF+CXt6pR2d82UJqZhjzENXSeqyJoGVJ1IU4Aoy2djhNonKJaiFJlUUsnsdjuBuYwhK6CoWcfBfOBpULNicqatOqNpGsfJ8QKN5uXLG4Zpi3OO5WLNOE5i0qfg3eVI00QWi4au8xgtk1oMEaywubRSOC+CMEVC10w1hcYaGqtxumKtR7mO/TDJ6+s8tRRCHAlhkmxmo7FKtAqlaEqZdRpktAXfgG8MBg0pkqY7SsoYbSmxSr5DkpwKY/TsYahxXongLUV5PZWEkaBBTMMqVVfZ7ueCVYo4TTit6FYr2qNHbDd31Lin6y0qjYzjSNN4sta0qzWqaXlzccmPU0J5y8/+5FecHC05fvgQ03puL9/y8ne/5sgndJ548/KG9ekpq+NjzrrE9f4dL37352y3N2jXUJLl4uYdR53i7PiY68uBNEXCEMhGY23DcrlmDIHGZazKOJ3wNqNqJtbA7u6G7bs31DBCSuiiqNXgTUcYCq1f0y+e8OzxT8hB8+76JdYZdJmI4w1xusWZAWsC1kTaRlGVWK/fba7BakLO3N0NpKLpuyXKTmA0yjqUXdK6FU+f/5L1+ROBafozrHvDZhskI8Qq0XyUwri7xdZEa+V9r1UgMaMLKQec8mw3G0oc54RHcYrVOZHynmJ2TPWazVQ5PTphn4XVc9SuiVmh/QJjDAujyTkRiyakTNsuWK5WNF0rtGOrxWJacX+YGiNq8LbpsM5TMVIUd5H1ek2ZIaLdboe1lr7vZzaRQE8pJaoAuBwYlnWmjpZcYZ7mnXOEIHGZh7PusKcNKVKrNC8gE7dCzxDRzIQ7UPer7Anvd3eyrrifMN6fh+HvetT/+1FS5YA9aAeYq1i5x9+kQzegLHnu3M3M/dfaflAkDrRVsVawRv+gah5uH6qkD3/XWpHTwT1Q3+8NDi+W4JfvJd4yNul7rq9zHvESkZ+3zmCtmf2X5EU1pjBNgWlKlBkXVApsEr5/VYrGmjmiU5OSSNmr0gxTploZY5dH54Q4cLfbE4fE+viM9aInTxumbSJPI6VkFGA9eC1xg9IUOEIqKNOwXDYcHR9hrKFU6eqNVpQkBazOQQg5VoLKON/gG81HHz3l1as3WK9YrhvOxo43rze0naMWhW+WxJAYx5FaO/ZDYr/bc3JqODteUrKE5OQq8FpJUZSmSpFTQpdC6zWd0XROo8nEmCFWjHYS1B4r4zASU5b8XjsXZCAU2YEo1EwhriwWntXa0/UaXRNxDBAgDlBSZZyzZ62GbEHVjLbzbkUpsdtWRogNRQGFkkSrorWwSVKphBS4urggpYAxivViwY9+/guqVvzNv/03xOFGIChvZsVqw+n5Gd9f7Th91vMXf/VvMM7w2WePcIDRmc3VBX/1b/5fuLSnNRnDyPlHj2gXC4ZhQ6smOpXYXr9it9/SLY959Pf+PidPz/jtX/8Zv/rxx4Q7zeu3N9RUsK7h6mrDpG5YnCwJtzecnVtU3BG2E1McmYYdw+4t03gFcUOY7iBHtNZstyNxuyGmnh//+E9ZLR7wZ3/+F7x8/Q2f//gT2iZTw45pd4W1I1UNkEf65qDViBQDWUlw1jgl2raX9y4UioZ1f8zTj35B3z+lXz1hqFBL4PjsOQ8e77m+vebbb17x6aefM8Q91xeX1LAhjRu81uyDZDt3nRiaxBywQbzGciqoDG3TkMKENwpiRKWBMRRi0CyOjtHWcbfbc5QqVTu0Eyi0bzuazjBMI71rWa1XuGaGcpwlJ4FejfWQEk2jWC6XLBdLnPNilx4i1sgZcXp6ynZ3R5gCW7ZYKwlqzjXk/N4tOqf3UcP3Wq75llImpZGUCsOwJ4SEs56+WzJNEzln+k7yaSQIzDDLgshZkBLn9L27Q6kKyQypM5JjMObgLTfbXMyWGPcisz9WUaDWGceUcQQO5nV13uPV2RsH0Ap9nyaUf8A2em+vzfsHOS8dD7kQHxaI94Xhh3YX75XO9R4emmc01LwE10Z4vdQ5Q1WbmbJ6KCJyEBmjZKdhZjfUKmrYUhTGNnRdL4HaSjFNI/v9jjwbwKUU5scuRS8V8blx2mHdgm5xxtHpgLY9p2dnkCZS3GOcRxVHTZaihdE1RUWqmmwMMRhcs+T47ITjoxXGKcK0J0dFSpEsgDxaKUzVxCmRXKX14g6ZS+Hq6ob9sBHcH1gsHW2jGPcjqe7w3ZIQCyFqpqlI15YLtYzobOkaTdOKeEop0E5k/SVnOt/gtcJSUCVAAWss2hnGWBn3E1NIwrQKUlS1UnMqnyjatGI2+hJ7ZGUqxhYWS0/fVWqOqJxAg9cCv+nRzLipIsQqViNKVNIVmWQPLpYxZYQtVw9UOGoB7ywpVcIUqFV2O8NuQ8iR55//hNff/Ja3+yu0tWhraIzHLXt8A//sP/+PeXl5y/XVV9xuruk/eU6IsNtlvvr2LcPlOwh39CtNxtCfrumPTzDXGza7QNmMKB3QeSINd+xvXlPzyO7mNV99ccvm+i2UCTGB62hWp9h2MQtHC7eXr+jMMSUUtuOWWjPrdUHHzHg3UBnJdaJkT0wTQzasTh7RLVe8vbpmGEb6xvP21Xecny9xBj7+6Ckvv/5LrM90XhbFmVmxjiYXTQgTzlnCOIo6Vsmk5NwRDx5+hvXnVLOkoRLDlrZb8vzjH2Ffeb7//hVts6RxljhM2FqIU6JqRdsuyFNCNQ0xRHKttN1CfI5ykWs3DnL+pITD0ZhKJaLbFuKOfnWO8tJ9+8Uxrl2glKPtWow2aLsnpgmZJxVt02KdFYJIkPyT5WKJNZa27WiaRthKw0DKmUqlbdvZWl86c2OFYHF3t7kntuQZDTlkvlC5h3cOZ2GeszmmKaCYA77qe93BOI33ZnxC5Zd0tmEYyCkLfFveJ7gdXFDFH07Pfz5YaFcOuc21lvvd7x+tKCh1cB5l3h1UqpFsUT7YFxxu+t/xNfUHX3vvcjoXAN4voeWwNvfdv7r/UH+wjyj5A9qpRN5RK7nI77N27hhrRiuLmYNHSj0E9HAv+AgxiQ1nLfPvVvIzxmCdo/EN3mlQhXGMhBioJc0MJk3Vek5nMsRpmPnvBtOsOH3wMf3yBFUm4S4flItaJpWsKjFBUYaiLVOCbFoePfuM07MTUp6oJdD2hhwMu5lbLclNslCqSjGNEe9EIW0d3N7dcnS0IowD0zjgHKzWluubwBR2ogZNWmx/bwZSKngHjdXsd5k0BqZ9pekNxin63tMYRywTjXf03qNKIgdFmSaZBoAcYdoHMaeTZv1eL1BnqK8iNN1aisBlQpoghUQtSczfrCeNIyEctCcK5/T98/XzB6AWgRRzka8bNKq+948RuPJAi2ZmbShCSChdsUaTwsRXf/3nXL38mpoCn332OW/evGQYBhYL8ZjZb6548+1v+e67V9hGs/aVr//qJXGC7aBoFg/401/+jL/6t++4vbllfdKwWB3Trs8Y9xrTBooOVMTx0+rMd19/QY47Ol+4u36FVRImo3WHNS2Pnn5Md/Kc1xd3lDhhTeLu8numsMW1hpgDIztSuqOoCevFY6sU0L5Bp4ZutaJqRbWKv/eP/hRL4PWrb3nz5mvOjnuePDzn5s1X1HQ7O3s6QspMBciGu9uJ6+tbVNSz86oIuFTxhLGSiybFxN3NW5pOFsSUkWXn+PT5M968uWB7t6H0PTmLsKtiCTHTWI/zLao6ct6BNrNQ0qC1xzth9WTEQttQsUQao1ivOnY1oWvi0dkDTNfSr5ZU42n6Ndp4OYesId1l4iRdv5+XzXpmxYWUaNtO9lFKzOlCiFxf31CUOCoLi0rIKH23RDs/M46kEdHFSDJj+YDcMNPM733YZuq5NKqKog5+cBIR7Jydr+OKs40w55SiaztSzGj13rut5Fk3M8NrgoGJG/QfWm0fGuo/+k4B5A4Pzn+HBYYc9Ob+30DNnuAF5hxfGV8KWr+/uwPLE+r9TuF9kIQ6MLDuGUrv/63cY2qHwhLjLJAzMi3Icy8zXi0HinficX54I0sp6JmS9j5UGxRmttBu8I3BGYOz4oGijLijKpUJccSoBqdnYRMTzDz8mCKqygdfFS8mWk4zbt8K46ZUakpopWjalnGYqEWTi2XKCr885sHZJyyOHjIlcUZ0bu7czREqR6ZNme0aZkuGDGnKDHqaZfAabSsPH57x8uULvFeYox6FobDl4mYkhD2YnpgSUyzEWGfs3ZCjHKDGO+IusJn27P0gXWSKwsI6PkbVLE6pU0bP5lu77UCN4OcozIzoXCBzD3oKixhn5SI8mM3FUNhtA33bsOyWWF0Y4i0lKrxxSHCOdGAVwaBzmbOm52k1Z+GAG/3eelzN+xqy0P8O19jhc6JqIdxdcLm7oihIx0dY3/Ls8WNKqVxcXPD2u694+92XEubUaiYSzjZMo6b1Z3z7/QtKTYxjwMTIovd89dvvOH5mudvB8dNfolZ3s6/SxLC7wdnK0aKnTIlYFcNuxBkrqV+mYRzFtFCO0oxDFMQp7yDD2eOHDKNjHz0xKVJ25KKouqPqJU1/yhg1YwG84+31NV4lHj//hKIy3kR2k1hoWNsJ5dEqwjgyTJFxXHD1ZsdwN4lbMKKhHItmfdpRpsKw26J95asvfwt54Hjlab3BasV6fcLpTz8jVccQxKhu3N+RfcTS0rYeoy377Q3aOFCGYcos2iVGJbzJGAc6RVJU5FQxZEydUGmg5oRqltQcqHFk2Xt0s8C0PaVK1oP3TrQck4RKKYAiJBRnDTmN1AxoTS2KVBMxBlIK4vGVK6XY+10mc8a3EFbszG4rmDzrC4oI00DEtGqOy2yb/v7AttaSUmIcJ4yxM2VVieNyVVgrEcFyxsrkoLW5T5UMIUgaJYCKc2N1+Ln3qn6QPet/kJAdoZJmJKr5vaGdjD0iQNNKrKmrqqJcnLtu+fkfQkbzV+9Vyx8a3PEH08SHA0dFGCaHfQbMjCQtv09ewMPjm9+smQZ50D4ILieOngdRyaHITKPEflrrhK3kHG3r8V6hVUJhqUiUpsoK6y0xhrnKJ5QSP6UxZPHMSZUwia2z9w1OL9hOt4R5T6G0xjUtxVrGXcUves6ffMpi/ZTdBFnNgpcasK6FOgm0lUVx7K0Rz54KqihSyGRfGVXANYkvv/qS7faa1rcyAuMZQ2EzSTYA1YBRLBat2AbUyjhFVA7QKhoL3hhMtsR9JNZALYUaEt/fTRgKVlfp/Q4wXDW0TtSUVWlSLvPUVsg1CJVyLuB6Vt4731CAMYxMQ2G3zehS2W8r+y3CbXUW17Rswi3DOElOhVFiMQAoUykp3192B0RRGyW05lpIiHlbLfW+YKScManQWS0OmNowDQMPnz3nV//wH/HN73/Hdy9e4HVBl4SqkTbIlFZ1ROUGv7L86h/8Pb7+5gXNcoEeJnQ1XL+6ZggdP/oH/4zF2UMeGoUm8fr7r1DlFK8m1gvD9cW3vP7+QvyfiiiFj4/OaFfHtP2KPsK0GShpJOyuUWqkXSxQZUQrS98/II+WOu0IJuKaI1ZnH/PkR7/ibixsdoF20RLRfP/dK2zb8uDJc+JwC+EKTI9SmapGEZkaQy6By3e3bK9GdFF4I4LDqmAMlRIi+82Gi7cvefLJJyyaxO7qNdspsSkRpTTD6SM++uSXGNuwWC/oj44Zhi3bzQWmBOKwxQM7NljXYJ1EVrqmw6pCazOuaFxObIcEOeKtnDUhDZL/vNswcUlTPSUH1qsHZO2I2YpfV9F432KQPHI5PCVz2WjQ2hJjmpuU9/nv6/Waqt8nqcm5YOfrWViA9cCunJmNpogbMwftVq3oWTTpZo3EOIp9xYFKfzjntNY418CsmRCNVRFCyEF5P2spmmaGw4vAW4dey8x0fWMk7Ot98tp/EEWzWC3XWsSNVJv7rr6UCmo2tMOIMfb8AFJM4pEDHMJuDrdDYZCg6z/0TjqMXVaW2gelcs0cnE2h3iugDwXiQ03EQT2r1SE2VFSHad5zGHvwBPngjZl/n51l4/qDKDznW4xV6CBwVRhGdK1U5CIRW/+Ks1ZStypi3Vyh7XrWncPUJbZObNKeGjaUPGGsg6ywbcv5g09Znj5lTA3aK1QYiWFi3bcMu4F3L7/h5t1LFlZxsuxETZmjuCbWShgn8Yqn0PcN07TBWjHtKjXjGk+/8KwHT4x79uMWpw2mayhFk4McerpK4Mn2dpLdgdG0zlHJ5FpQSejH1soUppkX+znTeQlhLgXpdErGaVnW5SKpbYfgImW4dyJVGMoA41hI0bHfa+42mf1ONDBhnGAX2e8jqdRZdGbvnSC1Fp13jgVJN5Sp0VlN23hCiMSZRZXrbEA2j/WlVEpMpDKAdaRa2dzd8uLrb9iNAbQl5YBOYlLXmf8vbf/ZJFt2pemBz5ZHuHvIqxKZSCSAEl1VnKYVm+ymkT2k2dBsfvDwJww/zLA5Vi2rS6AgEkhx84pQ7n7O2XI+rH3cI9FFNmCGdrPEvYgbES7PWnu96xUFQ2UfEsYqfvDpK17/0Y/54z//Menjt/z1v/p/E48HNm5gOUzsn47YbeYQF3qvmZfE/Ye3fP7mUgRo25FPPvmE9+++YTmKQ+7N1RXddiSUicsRlqJ499V3WCPut9N0pD56NtsfcHn9Bs8Hfv34G7TN/OCHf8qrz35KMhteXGxJOHLVXN8OXF4M4r/lLNcXW54+JpzvcURUiczLTE5weJh5+jjT1R6tC52tUESkqICcInmZePjwLS9eX9LbgOkznSlMxwMxZOLekMOBXD2oDjOMXL7YcfPyJXWZ+M0v/o5qLMo/yRJ2CvhuQwyyS1LOUYkM4444BGJ4aLvLTAwRf7FDe8t4s0ONHYf9I5uLa4pVEjiTMjHMpDDLz0TJE7fONydSqWNiNqfAOkBjrWe7HViivB5iN6Hpup7a8P/zsleIHhISJrUnRYne1Ep2Qaqd3sWywvPQdBArdTTG2GwsVFNCK2lgrYl4350OtUqZRneVbIdCbXsG+bynZnERjYRjrUzL33VY+J2bQg5RXCW1Fqy2VDillDVVMbIwFFM6yQ7VSqHKM/GaVF+eDwRKnQOypYA3aXjLhKhVvFjk+0AhLpyoc4iFYHPlJABb2SjGyGn1fJ8SgJFzBEx7pWzD6qD3hq7rTpQvpVRrclYsJYoB02N7aQD7h4/Umug6R8yid/D9iLUDm0Hi9j5+fM84evphxOSe7XIkHvcc40IqAWpGu4HPPvsJ29sveFocFS2KWiK97UjTE1//7O+4++4bRm/oLjYoLLkGmXw0bbQPhHmPLYY4gbEepT3ULMrlPOM7w8vbnWhKSkTlTE6LmPPpRG8tThcGLyZfQpktOK0w4l2GVrRmDbYmwZitFVm+TvjGMsqp0FWFVkGcSXWTxWvRFaDAWhERYozYGXcjfryR/ZA6EOsRo5DXd5H7XwIMxuDt0Ca4hNMS4BLajsKLKzldb3DWEJeAU+CsImQR0Bm7odoe0xny/A21CDyR08zD4x1P//D3YEdyf02JiT/5p38KhwfCw9fMh3cs1rC7ucSZhfDxH5j39zy++5qeR5SO0oiBD1/+DZrCzYtb0pwIyxFF4fpyh64zBoNWnpwsXd8Bmr/967/ip3+eubi95bv3XzFNH1D1veyYdM/rlz8FsyMumnmq5Dpgx1tqnfnNh4/sPv2CvpeDjTMD3l+CWdgfD5QY6XpZxO4fHuk7i62QpgVyoRwr4a7glop1pQnYCq5z4rtkCnMOlHTgcP+Wu7cj0+EOz4IhsbGJojKKA56JzI6lFGoxzEtlu9niNxv+6M+veNp/4JDg/vgrNJn9YeFic0leZpa5MvqRV59+zjLD/WHC255lmtHacjzusW7Ee0VSmbzMPLx/j/ITuepGmdekLAjAEgJ+8Fx1V3TassRITYF5OdAP1yil6bsNKSu0HnAmYYYNKS+UGkGJtYXWGkOllEQKUWCpKlOGURrrNEsROFMbWQ4rZUhJxLVd1zFNk9ScFgymNZSaKFgUikTGdh5HZZpmmXC0XJMCgytKll2suCcLC9Q1mL5rYtmT5c+zA/kfpCmcsH7WQv1MTMZZrSwjysowys3+YQ290Y2GCistVeCfevq5czrQGiWoTt8rJmznx7De79kLRMuy+dnEsEJWZ9ZTPUnK1+cV5xlrLZsmTBsGYSA8x+5yLo1uKwvorhtQtXA8PDEd5/bcDaZzXO6uKbky9J5aI0/HRw7zEWM2DLYF1O+uKXHikIMUWTewubyi3+yYKxRjqGFGGUWcZr799S85Pt2x7TsuNh277YjSmZiKML3aGkapSkmJSGKeKpvtiNWmBbq3BKZcoCiuLraMvebpKfLdu0fIle22wxuNVQaDLIaVaYE4nUfVREZjNaxZ47XKydyY9vprWRhppVCmecK3NDQQDjwNgkQX+beqKKqinKUbe4bNwHG/p5Cx3mCBahTaVOqyTgLtdNQm1XlujcGK1bjtFbFU7NZgnKUWTUEx50IdLMsiObvD5Qs6b7n/1UcMCassURn+67/8F3x3t+er7+64O2Q++8FnXLz5CfN33/Duq19iY5LXNgaePrwlHD4yHx6J0xNQKEaTjUN1PalmQlhQZl2Ky9T8/u4jg83EwxP3Hz7Sdz0xZEqZufvwxF/9qye++KOfggrM4R5jIpvLHVMoHA8T3eaCu7sH+vEFF7e33D3NfPGDH5Ibe+aYCv2QMLZgTEctMyVHSg7cfdgTnGE6PnBzdcG83xMOkJbEx/f3kCpD52Vxj7APjZYDmi1GwniWGT1Z7t+943C858W1oyI4em9Bu4otM96JRiSlgOkHjBE8ngq7q44v/sigTMf7t18xzRP3jxOdFgfXJQTS14HDstBfXNH3A8UcKE2h/vLlS7rNhiXA1dUVXb/j8RCYQ5SQmwYpz/NEyoklBvaHPf0wip2+ERfj4zyz2wzUJsjLuWJsh1UVUwwxTojXjTAva61CzW6Q9PGwx1mH975BSM3jSBuU1aeaVht8tUJHtqU+KiW6KV2NZI+oc3iUWUVMnOvuur9VVWHMChM9g9TVapy3inv/C8RxinitriW7bbVbQ6jCTBJzuowshOU7nxf22miBMrWvWQwSlChUKvn9p6W1WpfHmZwVukVVrj5Mte0utdZteVlPC2x53P/pc5HFrTu9KSEElmURJlVjJglu17U3LKO1FsFUrVgnLIVx3GJfaY7HLcs0kVLAu05MTIvw5LWRbNZ5nlDWYTuPV9A7xdAZtIX9Yc8xZB4en+gvKruLS+p+IYXK/f0dH7/5EpMjn336mk5XumbjMM8HJIrPo0rBe08/eLHcXRaORT6watPT9RalOsqciSlRqxFGlbegDA+PT1Ay46CxKKwypGWhloI1SvIcSkarSucsWhcxHjZaOOulYIxcCKa9r+t7X5HlujLy99UrV62BRyvWqcA5w/ZiwHWV9Ch2IH1vUCVT0joZNhtuRQsmCjhbGToli0aliEug2MJ2p7l6cYH3O6Za2ae9MHGw5NFiry/4v/0v/w/yceJ///oX6BqptuPm9obbT3+K3R7obyK//PmvuL75BFcsZntDWgo+FwqFkB5YDkdZFmohG4SqCdozq4GQHD/6wRd8+uOf4seR3/z873k4zmx9j1KW7777FldFs5JSYDrO1GxQNTIfMg8f3+J7TakzfjSUlNDVcjwc2V4KA6kbPV3fcfvqlj/+kz/jb/7u7/n7v/9bYql0444///O/JEXD/vAAaqL3mvv9B4aLntvbDXmauDvO5KB4+Dhx92HCNp+vU651abvCJqbyRvK+a8wcn/biH5aguxghJkyJUGfev/05fYDtyxE1XFJsYy3qTpbp4YB2l/yTv/jn3L16y7df/YoaDlSVOKYnap44hEeuNxu2wy27ccPjwxPx4wMuaa5efsLlmy8YU8fFzWdUNWCGyIeP7zHGENthyHcDg7NoY5mWhVTBuU7ss6viOC1sNgKCDL0nZ6hIQyHLZ62iTofR5zvNlDLWWayRZLoYU5tSpAGYZtWj6+qYYOj7/lRPV1ts47qW75JOELikSp5P+av2Qbc3Rj87sK9ahnPdXpvIf4GQne9NBq2YnoUZ4pxZ66onQAq9WqcE3XJ/y+niX1uFOpm6tXJxymvmXFQq5+bAWu5py5y1qTQzPbN2ZClUtuFyJ41Eu7/V+rvCSYkYQmj7ivOkIR1dsyyS7tX3Pb4z5JSozTrAGsNkvYSFhwhFwjDmJeAseN8zbnYY31G1QnlPbyRYpqqEuvvIcvfIx7uPXNzuubi+wqjMdHji3TdfE497PrkZ2A6aTkNeJpYwM00HlsaoGJx4Mo2bHvZFrDtiIkyzxB6qAectne+oxZKzpmQJ+vY9vHo1ctxP6FrQueJoIpiisEaMBGNYMN7S952857nZglh5L4ySpVfLLyLXTMoCsyUQP6eWumO0wSJxqUbJHqqoiiVRVGB/vCPmJ6zPstRLlRJlkWwbm8hoWYyvENR249ltB6iV5BTJLozXPePocb7DecdPf/pTwlz42c+/pN8M/MV/9RfcXF3wv//bf0PNFec6ybV2Hf/2f/vf+OQnf8qf/dk/pU4L19stH7/6DcvTPa5mnPAOBc7UGUqk6kqpmlgN2e8Ybz7j5uYHXH/6BfQ79iExXtzyifek6Y798Y6UKzkslCSRlr3XlJjZjJrLrkfXmZo1SmUomnk/YbotCrjYbfj8856f//xvuL39hIfHif/415ntxSV//NPPUVbz7v0HvD6QpkBvBOJ1ZHYbTQp3dCrw+HRHCollqtx9mCkZXO9bpsnKcy8NpmimjcpgMOQQyVPg4sUFzsI49FQTUDmhVOVpes+cFEl13P5wy2a4oFqLsj2xOLRVKJc5LkeuXnzB1c0nxPmJ49N3/ObLvybEPTeXF4zXF+QQ0UPP7XjBrHt0UuhuQ3UD/XAJdiQlRz8O6IcHjvOM0grve3zfSf6EcydX0VKkKnXdgLWO65tbnB2xtmvmk61Q6dqcFYqk/KnGeNSa4/FIzpntdkvKEuO72Y48Pj6KhQaVrnatAUiDUUrLAW4OxNVjyjRLHr6/FJZ61NiVdUVdWvLb6RD8j1P+V2rqyZfpd7j9zk1hNVg6G+LROOaxuZUKpi/LVfkgrSyhFWJYYSLpssIht1ak26X5zIjuAc7xnesTfbaEqOdms04hp8AedT6hPr+dYKY2XqxxodZayV2wtk0t586fkigBtdZ0Xk5iXSfeQqmm0+80OEY9Yp3hsD+ikd1IzpFKC7HPmhhn0hLpVMHVANTGIYcEfHxcmA4f2WwuMTXjNOy2A4GJw/4Rkyq1N8RpT1wCyxwk/8D3VJOYjwc6J9bb22EkloBViKvlNKOQyafzlpRbMtM8Y1Th4rKjc4rDw1EsBap8CFVCgkeMFAJrNF0nmGeYAkYjYUSrhTrNI16L0ru0AKFUkExaKx9io8BimoeLpciKmJAXjvMjTouy3PQaUpKhUmtKNBLw3mjRKxmg5NjGb41BolyHsWMcepwypDlys7umMz3vj3dcuQ5dC7/567/mZ//+P/Bwf09XZnoMdT7SRc/j3QNfHR94//P/SIiZ7/YHvNGYHNHxiFJiJbJ+KEuW+z0ukex6fvDZH/PpX/z3+Ks3HFNhCoklwe76E8hXHD5q3v3qW/IxwjJRw5Fx8GwGj9UK9TjRb3vc2LEK82he+cPGoazjy1/8nKVUfLfh/u5barE83UXm4yP/3b/8H7h79w3z0zeU+S3GOozt0RhqTOwG+PjuPYfDI8enB9JSeP/2kekY0dWQU0uze0bskHAYOceVLA09zgtRa8oykq1iOk5seo3VmloTvVNM+YH7d7/Ej1dcXN5i/I7iPEp1pGjxRYJs5pTpnGd3fcG4u2TOka9+U7hfJjYx47SmWo9xA36zxUcNpmtZCYZcxa8r1QrG4TohQAzNNbRZs+GMO4vMkN2jNb6J1DrZARiZiABxKjW9xGUSaQlUp0JrjGiiZArPbeqLgBBBQpQFsG9GetaIQaZSmpyk3khKWmp1B1Yvt7UmmWbkt9bEkwYhl2ffl4VJVQWqE6fWBl3wB24K65M/5TM/y0A4axRavKaufJ/+tCazcVoKrxYUgvq0KaAVE8X5NL82IGkQ66Sw9lMRiqx5qWszWRuKMAPkU/xc11BKIUYJr9BGM/Yda6woteCcbzsFzer35JxHa0MIiVKi4IS6ij1zgRgynXJYe0FJgtvnBMaK9D4mRQpHSAsoMSXTJeKsZth07NKGYwgcD+8I8wWd2/HyxRVX2454uOfxw695eP8r7u4P8jtqIeVKKRpFYRhGwrzncNhjrRKNRE7SiKsmh8pSsxgE2o6+G5tOo7KEmawydmNIcyGkIiljWqOyIldRUlpk4tNtZJWYU42zpp2Q255Gy+7FWENZkhSHKIU8N/psTplQF2mmiM1IppJyYp732LETf/xSiTPkklHW4HtHCG15mBJdZ3BOLIkPx5laA5u+E7ZRnJmnirWO3faGn/z4M/7t//evmB8e8SliSiZ9/IacCm6Z6GtALYXN4Nh/9ws21kKYmR7fknIVsd6S0DWKkNE4Epra/PFNmz4Nhk2/w+gBY3Zoe4U1lcGD7SLLcoRSePHyh7hw5K/+P/+AiQdsjby6ucWoxLzs2W57qi2kcMR2kvedYkJbz+Fpz7DrSA3CW/YzWnVsNtdcXY588823vPvFv+G7775GLUfC0wFnHXO1aH9F5w3VLugysxweONw98tUv3/L2q48M3mFaAqHRYvZWq4hCy2lU1+Q1LhdLipmwRIaNJ8bIeDOgVSGGA7vOkPaRmB7Z339DPP6QbtwS6tTOdBnfW+ZFBGtFaY4x4/0VP/rj/4bN9S0Pd1+xf/yK0Sm++/jI4ek7UtF0Fy+Y58SoLaWloXk/Qsnc3r7EOsvT04NoFZpXlrX2RFKRA61FK9EhTNMsvldK4jBTypRaqEqcFpYlUErA6HwS167ElOeMSYGchaItdVPUzMUajBIXZ60N49ARgojl1mCoVcz2nFF5rmuthqVm7Z1zs+LJp/q87kzPucy/u0YBfi/xGifIJbdA9lxqs5GWJLNV2EGuzxbKZyhGnlBbkChaV6unUUiB4KWrUK0tDow+83nlplaw7OQpv7qjrhYHJwio0btOYg51Hr9yzk06bk8KaNcoiqsycIXIjBFb7RBWS1vb8PCWAKc7UoQyB9C0aFCLMpnOOKgdM0eM9+iSIWt09ehaKFUzbgeuloVpkgQz3w9o3Qqe91xeXkF64Ol+ojarbt0YWpK1C6rvyCnJCa81UYUsnosugrlQsdqSW2xg1xm0dsyzGA+OoyfPR5xVEAvKN38iVVqzKSxxprOmQWutSiiBc2gnq5QyKIPvReWdQBqZVmhdSbWQS2Fu8v2uMxgnEaQlZ3FuLYnOGryXIHvnhUk2HcQw75jKKcxpzbSYpgJ1Bl0JFVLQuOK43FzyD7/6DW/fv8fWjDMZbyu3lx3TPPNYMyoVBq/QRJxTGJtZ0lF2LBZ6pyk1tJNvIlZDrA0UzRW7Is5Vc3g88tW/+zvM1R/z6eZTlOvaAWRHP+xQ8Ug9vufu4x6K4dXtax4/fMt0mEEFjJdYxn7Ts0Rh2onoLeOdJhWhzA7bHTEEOt+xzAtFaw5lwdcjb7/8j1QiZdmzVCvW2/6CGCN10RSTmZ8e2d/f8+tffs3Xv3pAF6imUlTBeFEXZ9Rp2tNaXHKN7aBUQkjoZtuwf9pzeXOF0hBLwRhwfc84bjmEB6a4EI733H/4muHiGtMb0PD4cM+4veT6esPxkKjVihU1Fa091y9/yuX1Sx6+6/j4zZekqsjVEWKFpXAzbFFIwqLVGmpq17NARVfWkkuie2alL/VBN+q5fCZBSCi5pJY7rkCVFnkpMarTNOOcopqmZG4upqVEeV2LBFqtgluQneXKzgxLxHst5pdV7sP7TnQFMUnkgBULjucw0VqTxBbm2WSQCjWfYf1Tff5Havd/AUXzGaNSSrVrv55gIBlPCop1262apJ3zSb7dSvMFX21gz11QJoDSgunXaWP9t9YHZMIwuvnBqNNjWn3Nny+ArHbfew5KITF47QWSC211IywoJ/eV2kjnnMd7mW6M1nS+k2QzKikXTM3Cl/de1LupktLSsEiNoYlLqoimNM31sQpeW3CkEihYNrsrlF6gitd9BXT73aaM3L54g1ULyz6JM40p5KiwuojHkfd03lBShJLxRjImSi2QG3lBC201U/HaY6wIu7rOkaJi3PSkJVLmgDFi721a2L3W7nQaLsqIx3stpJKw1mCd5vb2hvuHJ1SUYPNPP/0hv/jVrykTpFqJtZCoYkyXCylmjnHieIR+0OwuB7yxlJRYUkL7ih86jJevpQL9xnPYi43yYQ5sN2uyVMVYCKnCMeAuLzhkw/x45Ju7v+P4+ITpNSkELraO3djRD4r9MpFqxHWG7DSqZrIS2CsjIVC267BDxyFFORWjmHJmjnLQ8MbgFbKT0Z5lCsQ68+HtO978aBGIo8pF640DZXl/90hFs9teMM93Jwv4fuwwvcY4h7KG2paE2jqsgG4Uqix5c4euCVUUNQWmQ2I+wDh0zE+TZE6XgM2emhUhLvQ7hSmW+TgT9hP37/Z889UD3oGpihRTi18d8F0nJ1uTKARCyGgstWppFu1wF3IhpkgpgYomVtCuJ6vE0xyFbaYLORx4vPuGH/zoCzo38OW3X5Gqwl94+uGSmiHEQkVMG0OOaLOhH3s++aHi9voNv/z7X3D34Vu8HwlTpesvCFmsN3Sjt2MsqvmvpSQ1RIwvTROPxWatLkiE0S0tUBXiMhFYp2FFDAKRe6uhO3/WtDY4b1EqsiwzyxLofXeqcWdhr6GykmqUNC/jTqLalSVU2iJf7ree4O1lu2TqAADfQUlEQVS1CTzPSFgPrHUd3xSoKk60uRFMhKK/wror5eM/f/u9dgpSVJ+d+tv/Pl9Aiw2GFHDVojNFHNZsZKt8/WT9Wkrrovq0YV+hpnUHsTa485/rCV417q06ddN1Mjk/7vNe4ERfXLUUjZ66jobJaFIUSblSpplStRDtJQL2hOvlnAizZDA4b8QS2zlyV8mZdmKPctLKUKvG6K7tYRQ5S8gMzQzOuIGhM2T2VCxFKRIJVTTGeUzd0g0GVfYc9IQlYKoiTpl5TszLAWMKvd+AsuLUSG67GrH+pujm9d6yZo2cSnSVU3/xhWhECBDcRA6RmqqcvrCSgawUujViu0J2RZEL5FBE5Wg1qogzaYgzvre4pFBiaoNGbKsDEpOZmvVHmAoPeeLVqx3ee8JyYAmFcTQYa4k54HsHZLo8sISpkQASQ68x3jAOjpwk7DwWTX95xX/7P/5PfPmLX/Bv/4//A1MjVxeOfnBoqznkiX2aiZpGk7WCoSMsm6w0yluGi0v6vucQAss0k5XhOCcej/J5HPrExhu8Fkw9Fkc1kW+//iU/fHiPU5piBbP2zuDdQLm65OkbzatXL3j367cMo8eYSiqFrtthes8UFnJVGOuISXKyQXy/nNMsy0E+o4jBoLOKkgOdtyzLwjwfSGnBW8kWgBnKA7V4wiHw+GHPN79+j62K7WjxtiPGxDJHsXZWGt074cPbAasqKSlKcRSjKVqae1FgvMXbIlCgUhTbkWpkOh4o2qJMQenEMt9zeHrLfn7i8d092nnexQP95oaHfWLcvMK4LUo7tPYoA3Oe6e01Zjvwoz+9ZU5/zfu379hdX6HchlQdqipU8xObQxS7kLZ/ktCbQkhJLNe1IiySobzdXpwcl2OcmaY9tRZ85/G+R6nSnA16nBMbnJyzkBKcoxZIuvl1adMmEHn9tdZc7C4xxgO0Q2MixoS1XphNra4652QfkjNrpv1zWEio8edchJTExVkW0yuastbM71tbKCXT9O9y+70mhfXBrwvb2qCVtSGs2NfzJXCtNBvXM21qpZuu/8npnxPl9YzN6dOItd73+ufK010VyL/9GE+Pp3yfllpK5bc1HM8ZVVKpdYOkzo9HvqfivQTe1wrGdpTaUuCypihNwaCUbyuQAspJaHytoF3rbBatbRtZAykccUaUt+NugqIINTLfP5Ci7CZsMaA9xvQY64TSagyLiQLLpMKyzHTOsuk6qhGMtWRpzNIUDLUIZdhoIQc4a5ubrFxUT+pRQmvGjvl4YD5M1AzOWEpM4iukTFOFiw8LtRBzIcbM+48fURZ2lztSzPzmqy+pSrKujUKyr5VCO40zgLJMSyRlSKqSYmE+RLzxeDuS0sz+aaLvLJdXV+QpcnW54eryDf/m3/wNj/dH8Sqqkpw2bAZyXASWWhImaz5++y13373lL/78T3j4+JYy76m6MIWFUiCriuk0KRV0KaLOlk8mWhuK0uz3B0LKVG3JKJalEJtFvdKQiuIYC7FpLZxx1Jg5zA88PH3H5WbEqBFVLWnJWFu42PaIj9aE7zRDZzEm0W23+HFLUaBtZZqPdMbRj1uWSR6zdZY5Le0aknhYgfOa+0BZQCe0q3TOkZEl9zCOWI4s88TTXeCXf/9rpichJFDAOVmCGqWZpsw0RX78+ReUknn79i0Zea1XW4WEIiuZGmLJOKdAiTEhbqAWT7XCYOuq5JxM0z2//Plf04+3jOM1373/lv30Sy5vP2HOlniVuLz+lG64Ou0QlfYELBVPv73mT/7igusX3/H+/o6sR6zvcP0G6ztiqXz4cMdml7m+uUQrmKcgyn+jyCmeD7c1U3NCG7kuco7kFCQsSouaWkpDQmHQqpCaSEydGJdy+vdeFtE5CZQe46phaFkzzexOmkphmma6rqPrZLpYl/kpp1a01OkAvU4MyxK/NzmscPxap34bIjqjK7+7/9Hv3BScc4K3rZ33mf3D6oWEastixenU/5y+evI2gjMMxffxrucTyXP87/wkm0q5nnGzsxOg0Ma0PjeGNSnuJFhTCqvtaVeg9LmZrdOHLMHlBV3hpfMUJJ3YGDEss0qygU8NsbqW3eqbJbdH20KxXuJKlcaaHut7tIZKJIcjJS3UktmYJE6i8wO7i0J0lf3HA/eP93yc76jxEZ0znYVCwTrYbAcKsN9PTNOBwVu8t+iqZdmXFJHcpCJCoRSBR3vOVqGtk9S3zssHX1UGPRBiIM6JTEFZIw2ggLMd4llZ0AaUFruHmBJd5zkuE8Mw8vrqDW+/eSuRpog/ptaGvnMkayRTQXlizCQjU0eYErNLLUXOEOKCdQKnlJh4+/47nvYRazW+s8QUqVURY24TnSJm2aWEu0fe/s3PuNwNuJKJhz2dFa+mqrRAczYSjxFVKoqI1lX+roQBpYwsW+N0lB1NLCwzqKIYfUc1kq5Va6AaMetLJXF1+5LHCo9P73jxyad0tlDCEzFFdKdheSAtB/b7j1xse3ZjhRrQzjDNkbkktJOikkplO/akpbA/HPCDqGqrqijtKNVQqkBj/aCJeeHiYkNMms1mg8Lw9HCUXVKdOO4L3337xOFxwqgORcJZCw1y6DqPtSP7mHn3/p7jdMQYzbDZsbU9L24/wfuRnBJPTx95++1v6O2Ed5pco0BvVZPRKDeQQsJ6SyHSac1x/4Gxv+Bmt8VqzeNxIqnCxe0Fprd0Xj5TOYsNP9qStSiGl1LQfccnP7rm8vUkFiRV0/mxHcyaTXTJ1MYGyjlzPB7xnRNqtdFsN7tWbJN09lLQNcv1gdC1ayqSu1AK1VWUNmK7vRRSbNcRsj/0DpZcGk4r7qbWWmoRG39lFWlZTpkfyxJJSZIP1+jiQiWl0sSNLV636aa8921yqLJzU3Io1m1CkBS41UqowRBSIfkvktG8UklPhVvpkz22bsZOJy6sFo56Tg3iaZa0K/RSiyR5nVxXm7WyUM7PGQplzToF+fCrpmsQ/2NWP5FSms9QLWLGVwSj00pTtfglrTF5xhqoDWqqitp42MqIf49qE1BVtY3q+rQQp2F9Rmusc1LktWDyKCOsAa3AFFSztpbnm8lVGFtKG6wfqdqw5NjoZYaIQ6mC6SXbtXc9w/aGkgrb4ZE7Z7l/F5ifHkll5JASqVYMFtNZVHHYaglhZs4RFHQG+lFhssLiiAWU68ToTwm1U3IZEirPWAXOZ2lWSlNMRzCe5AdyERoqCkpSBAy99VijKOlATXuckVzfeCj4vmP0I7XbELXn8tUNj/cfm3pclrG6VlQKDM7hjGJaEqVqQDM1506lFdp4YjUcQ+bx8YAxhuN8QEJ5HNMkhIHHp4l5aae0LKygwkItDxynPdP8iNYZlQHvMNpTdIdxUPQBSzsxkklKIAaqpqaMcT1LyBynhXmBbAxYQ1UGZcTRsqrCUgPOKz755DP+7L/9v/Ov/n9/y2/+7j/w+O1bun6gUOm85ZNPbnhxPXCxgcf3kagypb03qWqiiH3IS2q+TzPfHr9t8bCKGALGNRKGlgwEqyHMCyrD4CuWmXl5olpwdmTUhjgHQg5M94GHD/f4zpOCEvqwMlS1ellpphhEcxIT266XnVnW/Ml/9Zdsr35AqgZdE1/oic+/u+Xx7ucY+wgqY4xklzcDI2KuqKxw3lGWGVMD+/07iv6CN5//EcMh83CM9BeX+H5LSKJNMdbT9QPHWYKarPHkmsEKW2i83JJqapNdOwSqymYzYJ1iCUe01nhvKKWT/UeMuOpY6eyrXiHlpq1qh9wUxNYil4rHkF3FG4P1npJF2FmLTFZFZWKuzY8okkMQVEFr8U7Khb6Tg+DJW42zY8K67xDG5IpSZBQao4xkW2hN70ec6ZhNM9WTmi+wUklU5EArbsL6xPLk+wqvP0xTWMVfJ0l2U6DlnHHWY40D0mmKqGvnarxmbdTpCZQseJGky7XRp+0DoIrRbGlwThO9KcQeQdUiJ+CWKbGebEA1Dq/w4GsplMaMkRe4tLALTi+ONNjmmHmim7elTcNtU6qnDhtTxnsnbAHrQMtFlKt46cjdtw8aShqFUigMaMFZtTZo18supKzBMNKUpLBUipLMWUpGm8R4aXGdY3c1UOOnxOmOj9/9hv3TR7yRe5uVIntFTJljlIvSKsFwrRVM2lQHpkdVufDnKvuMlCOUgjEVJ/WNYbPj4cOBd4eK7y7p+i3eOXrfo3RHXCr3DwdUCuz6gb5a0vSOoXco35OVIWnPr7+7Y7y65Uc/+YJf/uxvefjwjpxzE/UI9FTTjNHQe9FrKG05zInDXOnHHmcMx5TpimK8uGZ//yDxn7UwH2a8VcxRjPTmEKjKyCemwhyOuP0BbTN9r+k6zVwqqWphOqXKfs4cpixBPohFt1IWXSRkxlhHzIr9kjgsBYzD9B0xFsJcqCEScwQTGDaKrte8/OSa/eNbOrXg4hMfv3yLagKwrjfM77a8u+gYNw6lMzix1lhWAzMqXltqrqQKqhQiBpz46JRasMbinQPj2xAY2XiFNwFXIst+xlHIc2VKE7p4dNLEqXD//knM27REqlprn1mSaJlW0xGVtCj7m7ng9c0t11evmPSWYnoUC5pC1ymsEu2NafnjTpd24FEYv8VpT281MX6HyjPzsieQcZtbLjeXqAVCiWANKQSc6fBuQ1gkH3l39VKKXMmYfmgwa6XrRpyzwkRTsMwzS5h49+4BqPzgBz+QGqIU/TC067MSYmZekgQpjT1iHG9ANVw+FzBNfGsMuVbJerYKZ+WkL67RRnQM7hlrsYpKudZCSjMhTGhd6PttO7AarBFhbAiBFJMIUZUWvzIldNgcZ4ovpyQ4ow1WWzrXiegzCyEmlzahVN10POVZfZPJ6ZxF84dqCiho/vQC95yBemU12mlsW6as48oKv6zsodXCQiaOVthXLKzRTFeoaBWjySSmG3d33UFUSjnvF7R57n/0ffxshanOBnf/qSnUKvpQSslitVEtay2ND9zgoZQ45TsYg28sLJmSZNGnVhgL2iQlz8NpKwkTymCMb6O6kQ6fc8M0S8P7gabVUIgnSKkFXyJlcewuLuiHLU8P77G6Mh/36MOR+bgXdlA8YrxF20DKEU0zrtNKwplcR3Ueg0J7f2KQ5VzobQ9eEVLH42EG94KrH/wTLq5esdtdsN1cYt1Ajpq333zL+69/yeHxK3I9QrVsnSfFzPWLl/zP/8v/k3/3H/4jf/3v/x3//t//ew6PdxgiFEGympyBWptxnfc4ZYhFY3Tl7nEhV7judsQQedpPvLi84vblwHfffItTBmUMpYiOIsRMCpnUTljOOmHIpIzWGY2c7Pqhg2opxfK4n/ju7SPznNltLMclC/5tFVYrjK5YZGeyXzJzBqMqOsESK8dZmDqlZGxX6UfDOGx49/ZbzN09vR/57JMtVl3y5a9+QSqZzjlSeGD/oJhng+8sve9EBZ5lea/bAacWYWilUila+GulmatZ7du0W9qFH7A6YXKAMpPrAessKRSc1qQ4E6Ph4fHANEVx6CyVmEvj7sverOQqDDYym15DfqBmRcGzG1ybAFZnW0XOiaQS2gEq4Y2hczJZ5lihGi4vbhn8hqeHj0yTOIgGFbm7f2J7vdBtHS9e3PBwfCKlwMXliFUdpRrun+6Zppl+XFBKTtMxLnTdKNYQ2op9jHNCh82VMRe6YeT+7p77pwPDINbx/bhh3F4RY8sjCJKHvMwBbRTOCINQWzk0ZMSWJ6VMjQsoIZjEWfY5fT+yWvF3XdcssQu1CsRdCi2POTLPM1rJ7sFa0XY451BakZImxjOZZr2tmqpSpGVhvg/F5yzitXX5vbIu1/q7knbWOve73H73RbOShnDG+qXQu+YYWJrBl3TKVYvw23j99wvySQgHjdnSGoYSFkEBoZe1/rM+pXXBnduWfvXOWZvNGUt7tlvQZ3bTqg5c2Ujr41q39+sSR77Ywr1X1bSClCLHw4GUE8bJEsiapqZu7CrRXdS2iGqU2qpRqsXpFQUYoAiGrVYxWFu0GxlbVzVxpRBzJMwBQ8VvbthoR80B7Ei1B5TrYRip8yPdqPB6IodKLkEyGUrBWcWnX/wIt7ng119/K8VMGZyG3rgW9mN4+24iph0/+skfcfXpH9NfvGDcbLBuROkOUzSvd5/SXb7gV38dOHy8ozMdIUvYTomB//Cv/4pf/OpL0TbMM9aKg2QKgtNj5BXQiHGeWvnYNEfHAsd9YLcpMp1pw+XNLYPvOBwmnu4e8NYRlgwtyMgYjU6FkhUUjbGdTAR1JoZAjJkQK/0AtSbuH6b285qHuRCWQskwOBg78B6MSUK/bYvcVKHExHGKTLPkUStjyXkhRY2qFm8tEmM68eLFDVeXV/z6y7/GmcqbV7fEFJiXid46EUk1f30UrZEnspbinGKiKAtWt8+3nBatcmiFHJjImJrRJaIJVBa8bbqiUlA6c5wTpXhiLPR9z7QorNOMrA4DuTFfFJDRujJ2SIZBNxCLwVtQZLyFTKSyyD7JVfxoIVWsVXTesxRIsTAOl1xdv0BXx+PDEykbavEU47i7e+L6xUxkxtaJVIWN6JyjJJimqdnnS2Sq7PoMNzcv0AgBwlgvxA3rsdayuzB4L86jl5cvyEm0ReJI4BFKu6XrRznJxyhuploLU3Ke5KBpLDklSpYwK9U0CaEkfFMjS3Z9e7wnymgCCqUmQMRtEjZW2v5RwnNQkpUirqZns7w16tO2yAFhK0VM24XKfdJ0VuVEw18ngpWSf2JXrkmTayH9z9x+r0XzWijLWdbIql2oJ17tGjt3ZiqtRfa3m8TKItLWnPIX1tuar7DuLNosJEVcqdP9i8mU5vmC+vl/69fW2/dEcE09eJ5W8rM9hfw+YwxVr6f4ehqvxUI8iqeT0W3Mb34oalVaK842HzLaadWi+Ipq1FehECrKSTBTGqwUm+TdWSOU1S0M/UiJR8K0J02RZcnABpzC9AplPdo5qlmoWmF0puYjzltMUVjnuHn9mt2LN0wxkZYFSqKEhbwUvv7mA/d7uD8YXn/+T/n8j/8Z3e0PUcMF2voGzXhK1Wg3sUXzcv+Or/e/IS8PWAs2Rab9HV9/GakpcnUxctgvlCRBPCWKkynI9GKUQlmNcYaQNcR8et7TnIkh0fUjISzcPT6hrixFaa5fvOTl9S2//NnPOTweRSzY2B4lywmv6QQpRU7fxoqd935/JASIWYrFEqFoBy6AksD6pFQ7EAjX3DmHNR267SyWNFGXBbTkgC9L5Okx8fhw5PpqIxbmRvH44civf/4fsUroy1YHlMsoZTC6CH0XmZxKlVwHZZT4a5WEQmGUpiCfH/nPyKmZSlUZdIKcqHWh5gWlMiWB6XoSho+PYkNtjUVrhzIVZ8ViodqKV1ZSw3KUBq0UeI3WiYtdz7i9IKaOly+u6b2mqtSuj4RXkegjrmuOvUYTM8SicX7H5dVrum5HijAvlSVqSlZUq9k/zcRU0bFyfDigPIybHqU0SzvFj5sN7z5+y5e/+jkpZYZhy9iPXF33bDYbclGgmpeXAu8GnO0JMeJ9lT1GWaNZJYddmw6lBDr1vn92zVecy00IC9Z6tAVrPMZ1lLaH7L24IBwOB5SSA2oI4VTAJcf+bEQnymZFjAmlAiCBOlqrRlmXeiNBO6ImX+tqzpmwLFDP9fRUs4rsaPO6HzxJBPKpgdSaTwvr3+X2OzeFrhuayEui306n/BMFdS3C6+iiTkV1XUI/nxa+xzJSjbJ5WlyvuQq1sVafF3XEasE81zSc+bzrpn4Nz1i/RzQP9QxL1ebtUwEquURSltjLFYJaHx8ptsfWEr5qpehEisgyqBZK+73WaGKUZa3VhjbQtTfUnT4ESokjqDGGSKGWJH2x5U1rVfHWUYyoiOUzZlDKYXSPcQXfZWqx4seiLQVDxkqKWXqgKKAGfGfQuuK1WER89/XXPO1njg/3qBR5ur/n8e6B4zGxnw0LF+xuf8Lnf/KXbG4/h/EG/CgsG91RlW1UV40bL3jx8g2Puy3O9FjziKsB02lsV7m+vWG72/J0r5mODxhV+RAfz7BRe/+stgx9j04wLcfz/ilByQpdDVTDcQrU+kDIlRgC01dfkwoobZroqjQjM5ncUk7CkNJyYVonmHet0pgOx8y0JHlOVckuwcgBIbFGhQrDw1pwncM62THMITHNsZnGCcPkcFiYJzg+LninOO4fpGiEwKaDJSRqnslJikYIi3jgGE2MoSWCVHEFWDPIjW0naCFuoEUOm1MS/NsLPKaI1CKiR2O92HAUx8NjICTP9uqG6bBQmEkxtM+pFX+sIroVbcS2RFWwY8emkyWtH0fG/ho/9MQc0TriyNR8IOV7SnzCmoqzI9r1xOIxbocbbliiJjwFSoGvvn7P/uHIdiO7KaqhNKdj5U0r1JLUV/JCUWJ6eHlxyYfv3vJ4/xGjNB8/vuf6+oaSS8sMEbffUkFjsN7iuoGSCyEGjHVtU1OZjjMpJ8ahk8mdxuBKoeE3z2tWpet7SURT4tOF1c0q3jCOY/s+WG171oQ1rRXLMvP4+MRmMwocleOpluWc2s81eyAlC+gVZl+N7JSS6NBV96CUOglutTKnplByPk0KK81fbitK8geeFFSzj0UroWSVNTBeVr3CLJLA6jN76Cy5XjNJV3rnCt+sT160zW1fgSxAhQlUyTWdT+e1oItq+GcbiMrZOkNVKdo1C/20cn4tfpvaSrs/+RWSg1uziN2M1tDGxZTk/o1RrLzkM34HJSecc/iuY7XT2D894axlHDftzZOw+E4cusRQywrff7W71abKPkFrMUTL4sRqlCE7T0iRdYmttZUkOGNJcUFlj3Ge4ntsXVDJUJPG2Eo/aGqeMGgGp4hL4uMvfsHjxw+UMPNw9yh212YkV43qNrz84Y/ZvvwEM+7QrkM5B9aeErwUlVgz3irwipvLDeDoCxLPaRMpH1B0vP32jjRP6MYCc1r6rHNigS0vhWIcPHXKWC1MmpISqiJpcGisdsRUuOoH8iaRY+Ll5Q1fffkb5ilQlBJ+fDMZM1Y+O7UknJFFhraK3a7neJxRSuNsISz5BMPQplhaAXbeoGsRmKsmbNWYAjEXagkCL5SKcwPOD1ireLqf+OBg6BI1B7xTOG3ItbDpLctxwjqhNuZaWMIR23mZjptxWYwCWcihqjW4kkjTnnF73RK+KpCoiB6hUrDeoUrzZSqep6mQVc8//ct/xtvvPjAd36J1FB1OzbLjUrLwV/rsBaQqXF1dc3W9I8WAcSNJ9RxTRWpeQdcEdSZOT5giEAxuoOoR5y7R/opQO1Jq9SJFHvcH+q5nd3HJ9es3FHvZFP40FEDEYcsSWi2QIrjb7fjpT3+Kdx5rPa/fvMZ5T4wJg6XrBYik5Rlb65uLgJHImhZnWUvG+p55/8iSEs4bFI1BVCrTdMBoTZQTH0qJorkUhXc9xgnxwDT0YM1FWNGTNbdea7HG7rqBfJcbK0bL56kto3MuxNgM8NAYK89dt4V/rfUEFVlriUGW0WvTkTomcbLyfzQlpVNjkEO4PR3A/rF96j92+90pqUqLUKxWSsP8VcP1Vyiomvqs2KrTA1rZPyc9g5Toky6gFvES8s6z2sHG1FS4bbksJ8eKas6Y8lVYF8+qMYaoza1SNZWf0Sc/JEUbtWo+Na5V+KZb0AunfUnjIpjnC/J1MhFVstbNRlghVMGcyc+W5Wt8nrUOa414FRWxnUi1UmhUWc54ZSmFkjPOSBq0YEryfJSzJFVQyqO1IswiyMlV4/0oNuHdgC4LOneo4vFmR2cSJc1YJQ2m5opKe1SMdFpx0Ru68YrXP/xj3s8dH5YBt70iKIW1svTVNQhTCMHrVZb8XJaZOD+hS8DowmbosBQSkJJifziwxPazWXDv3gJNx6c0dF41WCcwz7O87xVKKqgKKSRyyLjRk3NiWSa2ux3v336HsoYf/dFP+Q//+t8xHeYmEGowUSuwrrc4lzGuUFUmlYVcAjEpdIXOCg1wcB7VKWLUpDChSoEQpUkVUDGBKpCNcM9rbAK3StdJ9kDJGm06pkOihMDlxuBOU6/GtiKvENX7OgHQYmZLLeTchH5FDNJyCuBEaV0UoCq5Fpk0q8I4RakJYxShVMZ+Q8qGDw8R39/SdyNFbZmXu3ZNVnpnybHI/sI0aEGJA4D2Dms833185JuPD+yurri4umJ7eUvUPdp4yR4uAVUjpIyqGmsGDrHDuyvggnffTty+ekkqch1Ny5FaZZ/hrKQzuk72AErLAS6XIuLJKhMS1eCcxmnDOPTsdhcY0/HixRtSqqSWGe+sbQxG3a5dQQVyI4bAWhMsw7CVYh+XBlM6OVei2y4jIZTO1UhTnXB7Y8QxGdaT/jm/GWpjHIFSWRTORvPixWvm+cDxeGy1wLI6MIgbc4IqbCOIDJuRWmuz0zY8Pj4SwoTR9mTxL+pqh+Ksw2olUupLUzors9psnNGUP1xT0BZrvs/sWU/78sKIY6Y8MIFrJP9YzJ9yllPhc3sJ+TmNNRbblriKKstCrdpyBdEorLRTpZ41FlqxVs9gofUfG1OpnIszSprDcxn4ujQ/i93OaV4Kke+ffl9d8x7OG31FlcmkZElh0gajNJs2IRhj8V6CX1QtpBxYE5RUkCW6MgaJtS9AFiZBiOiaxK21MQiMdbLwzIl5FsMwZR1j37OfnlqiGqiisNrj7BZtPTEd5MSldfvwRy5vrvGmUMNE33dUPXL58g1j94rtMsDmlqoqKS6YCmRFTboB303fkWZqOrIcH5mmPS96zyefviDEA8eQBQoKiaFk4nGPzgHCTG3+LtpoUBltkRS5MJFCEJgMseTQSnK+l3lh2PakPHP34T1xuxPPqc5xcXmNHzrU4UipiqyKcMmrTLXDtseamc5JE17mBWfUyQ/KakVOVQKMvCeqTCJDFXM0W+VCMQVcFuGbreCVoTNJImhlQMEYMTFc4gFvNdZ5lEpYsZalInkDuU1aKcpprhRFSLnh+RZOO7cq0Ifr6ExP7wa0FoxcG4sqsmikOeXWHEk4pqhYqqXvrri/P/D3f/8PGCWaFKsKttPEKLRrowrKSirdEjMVTSwwXFxRlcNtLtlcfwJmQ9UDoSpsUWjj6OwOla949+137I8TNz/8nKwv2V19Rn18hx9ek5cJTOTj3R25LFjb0XWKGI9ic97Cl3R7jSSlTzU6t2raHoMGum6DtR0V3bILjAjt3GppkeUaNG1fqAR1gHawbXVhO254eAiEJeK0QTlPbddvQQSYK/FFa0PJAvXJbhCMcTy3tl7rkEx2BqWSoApF4WyH2zl2u0tqrdzf3zPPE1pbcsooxMYlN6hLzfOptq46hmURZ1bxcpLGAMhO4cREkgZTamtq6/6hgNLqD98Uzt3tvFyWomcoJZ2+/j1713JenqxP8nn28Wk0MvKG52b0ZJwTBV9bjuQGvH1/R3CePM6eSWfoChok1H5en+AnYbucn8tv7UZOlNfSHBMlLtL57tl9arRq2K86vx7GiLqxVkl2kwLhmiNrgpLIOZwZSVXwatVS5SoV49YTU8VU0ekZLYyTnCspZjRgfc/28rp9MDM3gyMuYu+QllahrCGpVXXsyK1JbwaDURO7qw3peOT+4z2heg5J0V/uuLl6TfVXTFETlwPxKEEhsigxKONQWjDfp4f3PB0eJT8Bw/unyJwt1y8+Y7fZ8vD4SAoTT/VbyvzIpnM4XZiO+5bzbEAViQqlneiaAV9tg5KoUSc2c4/1VaDFnBi6jt/85kvmn/2DWFZoRaTgxo5h2LJEOX0bZzHaYb3G5EwKie1mQzRwzImyJHGanKbTctdrjVKOGDJWaZyyUBMlFmoqUB2d6tn4ytMcRSXr5ACzpIipGesEwrC6aXKayV7XOZaUOB5mtHV88oMf8Lg/8u27d2hrZDIxCGxjHNp0bDZXmPGCpykxLZGx66UJ6SrLanpKrKA8T/tAt7nln/2L/4Zf/uIrhqHgVCbN95hyQFmhnRot2hSlmwJYabRzGN/RDxe8efM5BYeyA9vLF9wfForqqdpitSPEwv3HR+4+fODtN/dECi//9DOm6HnxyZ+TzRPHOVC1Iucn7h4+4jrFOBrGQZOtwTmZuAsZZRTGCTU2l3wqbEJXF2hIKY3kpRs2u5HO9+RSKDnxcH/Psix479ldXuC7rj0vgW+Mcaf65J0Xl4a8YK0T2ugSBEqyFmsVKUVsE+amlo9Qm9Gdc2fq5yoOWwW+1q4Qkdj6n/zXjDr9XUzxLJ3v28LZscyBWg9t3wTzPJ8CeDabLcsceHx8FGpraw4S0wnH4wHQUOXnntPw14b1B6ekeu9PRdEYwdzOft1nD2+llBR5pf+TJnEq1M/w/Nq42CvftORCrYGsRTRjfhvz5/vNZ50c5M96+vvaJKx5zgJSzZDubCD123uG1dDv+WNGfX+yWN1bT9qGRs+VRtmU2LoZ6zXGQSliiZ1SbDJ3ObWHFqC++p9wMvgTRktMldQobCkG8VYpEWpueQISFrJxPcHISSg7h9EVpRbCAjhk0lKKcdzy8tNrpuk7yvGBbrshGs/VcE221+A2mG5DUQ5DEUYL4v9TchBGCHJ6ynEmhhnrHLUfmcKRD4fK1asvGG4/JZVK7TvGbSGmwkxms7V0NrOUmbQENp347MxLalTjRiqOsk8wVou3UEwcD3u21kGLrTz5v1DEhM8arHZsxh1GW8J8YP80Yb3iYmvF/M/C9YXnYtyxv58py8Lj8sA8Z7yJ0HxxMJJJYZTCmw5nDDlCzXJQKFVhbY9X0qBSiZIxohQxB4bR43qLdomud5QojVUbQ0oLoMm5ElPi4WHixetPmRbN02Hi+uqCMD0SlsJmsyNVje92XN1+gt5PpPt7tLWAUC11zjhTKTkTa2ZeCjj48P6OeV4gR3J+xNQntF7QVMn2JrcJpoIxKO+xygIWZTq+efsBrXo++fSKZSl0/RblNzg7YrTn6y+/5F//1d/y/v1XGJ25fvmCpV4QlaeaK65e3HL3y19CkXRArRQX25HLy5GhVyRrKVqII84L4aKU2KBUhUHjfYd1ommKMVM0aKvRyjCOG9FapMx+/8T+6YFpmhr9euHy6lqEtKVS0dQqrC3dWH9DPwCiFahUrDPNsNEQwsTxeGTwXSN/KLyTPZ5zVoJtTg1BmkJpegYRmTnWHGrZDSws84K1ms24bQXd4pzHuV6all5AKeZwPH2+h2HAey/TYNdxeXlJSonHx0eWZcE277cYIy9uX3Jzc8s0Tbx9+5bD4fA9e4w/+KQgNtX1NDEopVjqIr/EuvMFuhbsJi9Qz/yRvh/8sArUzt5JtW3wqRpVKylGjG5WuO3+RcQmzeHMYDpPIPLz6rSYPk016oytqVORX8Xx8jh16yTnnGjBGFGqsULaXqJWWJ8rbbl5YhCU5s10TnCLKVNKoJaJZZ6IIeG7nhAyDw+PXN3e4htOmaIEgVQlS/UcpSGUKtRE53tUMeS4iM+T8fSdxalASWJnLZGjiZo1xnWooshxIseEspZhu8ENV0wms+wPXL35DOuv2Ice1W1JyK5IlYJBEVVpKlUjpniNyUVRVOdQ20tseUU5AtZy/emfgu/JYWF7s0WlPUl9Qyiaj08HLDO5ZmxvMFZwdltF6GQMkpugI9oiHk5WYgqXkOiykfCeWpmnA30/0HnH4TADlYuLLcOw4eFxz+Nh5t37yGa7cHUpF7NG1N3zkjkeF3GNLYqaIYpOUPIKovg9eW8oqcjIUhCNSZVTYE1AVuJka3XTW8jhYxgMkNBKwndsp6WQIAVid/mCYbzkaYq8e3fHMF7zxec/ZYmJ4/6eh/fvUMUwzxGM57A/0u8WdpsLDkeJ7qztuvLGQ87UnNkfJqztySHzm1/9Urjr8cigj3g9E8IebfvTIQol4fTa9eA8rtty93Dg6d07XDeis6T8mXHL5uoVP/rjP6cbLogBumFL1p5IR66VY/JUu6VWy9OUub19xes3kXfvfonXkX7cUpeJvh9x3lHNQNGefhjodhcsxRIpJyqmcY6+HzDOgMrkEk/TfF7rTcsNzylgtGIz9pSSSTmRUsRrvRYWYXPZpjWKixwUQQzytKQA5tjSJaPsApcYsM7RdT3OeoGtUgJWvzY54GktiIXA01ZcHqxDqSauNB2lrHRVYQcJ6cZidEFrQ9f1WGfxi/h9TdN0QjSUUnjvT1PAdrvleDxyPBx48+YNNzc37LYXTQ+R+eSTT/j6669Pk8Zquf2HbQq2MYpaDJ11Vk49MbMsM941ClcSiXVWBaPlolfI8kPVgm7LtNgwMWstxRTSapAn/KzvTQPOuRa0I4wgYy0pxtPPKN2opc2uYg2YWUPGtVJCYG7pWGpdjKfSBCRyQsi1iD9RO/WHHE4OhbpNHDWtYT5rLOkz19jUQn+aNWsqgWRSey6JWiJzOIonlNX4zvHixRXOi0q0ViXLytj2BVq1/AUHWdH1HqXB6Z5krXjvGMUcAqXMUMB3XaMrLsQQcVpsLaa0Z17u+fWvv+Pdt/+a7Vjou5F+uEa7kaovubi8JbIh4ViyXHjywTWgItoUNs6cF1tao+wVi6kk35HmN8zzga/uI2/eXFOsxprCvBw4zBlsT06Rkio1FjAQDCjn8ZuRcAj4CtUorJvoellhdIPQAXGa5AeizpQ04Z1jd7HjctwyPTxiO8e2ZRHMCfyo6LcKZQq5ROZQ6L1oDI5LJKI5zDOxfQ6KUqAdRWlyTU0JLhPDMs1NVKfIMTHFiWSEJaSRzAlKoS4JbzM7NzBYuOw7hq4yhdjgMU2KhQ/vP2LsQFEWXSq/+tnPuLz5CMB0fCLMR/m8qULVkcO7BWU0X/z0T3l1teP+/k6gRW+pIfD0+MjhsKB9h0GR4xFNgTxjSsAiQVid8iy5MoeAcYaLccSP12B3RDqs7/n49BUhfsTZKJ5Uh/eY0JOmb3naQdi84hgclzeX/I//07/kf/1f/18s00Em8K6jHwZMZ9C2cPvygocHzzJDUTt21zvobohGQ3/Nxe3nVH/FHDO667B4nLcwGHKSPcvY8ouNjtSayBXCEghhz253Qa2V43Kg3zT2X0x03QCIe0BKC8syt1qRyY0pJMvhwjwJo8d5hzaGw+EgmezO0XXjyZMoF0EHhKV2FsHK8ti1XYLGaNv2GrLniGlBMusruUHtznWnJiKNRFIetdJYI75IqqrmMC1MsXk+ME8HjocntNbcXF9JM/HSsFLO6BhP0NHr16/58OED8zyf0uH+oE0BhQTbNJpoSlBLauNJJ4pVIxF9KQZsiwYT11SBV06wUTvhSPcKlBhPW3mlzklr6ylfKYUyLcugcsLcTKOvynvUoB5ZLPwWvKROkM/6/1c1NPA9/YTiuQL6mf6icpoU1ruShYJqTAX5nuf7jhUaW6cSozV930NVpw9RrSJSenx8EtiDs6JZ3nCxpw7L0l4bBSrhfc9mM1Jq4unhI8dpYegsfT8AlWWuVJJ4ysfE5eUVL252xPBEmN6h8xMpBiZm0DTvG4VRIqAyVIwVtkuIiRqhM7aJGOW1y0umZOj6S7wdiP0lNh5xTjEnxdBt0CrytF8odNTqUd0OZSCkQCmSF0FVeGPRHsI8EVLFeIcrYlVdO4/1I24csWNPIVCDJ+Qj7+7uWA5HrFHEEgnzHm8Lm9Hz2WdXvLoJGAolJXLURKWa1kFTaoceeq5eXUHRlJpw1uAczMcn5uMTqRRikUVxKbXBm6BMpaqIsRqPZEjkkkklyundaTon2dXLLFAr8tEkR4lfzDWSqyhnO6O4f/dtI88UFIWURfmtvYKc2D/cc/f+A7uLa7ztyVWYWN999448H1DKYaLw+rXK1DyjiRiiTNPagXWkIH4/Nze3DOMVUQ0sZcSqAbTl6jpidcEwodKRzibRQCwL99/+PZvrCbv9hBwdr9684J/82Z/xd3/zN7z59IdsLm4wfsNwcUFWEEttLrIe211xWCY+HsSCZDdeot2V7C2UZpoXxs0VxnYt+lMmq1Qa47DtoIT1Iwe7ZZllL4VM6tbaFqe7wbSQGmtlCgghMM+hsQfz95bEIS6gKt55+qETgkxLF6QxekqW7OUVNjLa4L1rwrAW66kbXFUL03SUDIQSSSmc9Ayr9Y1YYPiTpqrrhFXIoXBMsWU1C2ieUmK/37Pf7/Fe9iHD0KOVpVZhMZkWKrTuG4Zh4ObmhsfHx1Mwzx+0KZxsIXQVt9hWIktMp4Xq6XSf2tRQi1gbtA712yrjGGOz4y4odbbSlpzmtXvKaVU/O5Gf/ZTaAoUz/r9+38o2en57rmZ+/gI9V1jnkr83sq3N6fyzwgT6xzIkdOsc37sfzrYgWjk6b9oUs2YdqBNXeeg3AsvQbB5YFa5ReNNItqy1Qovre8s0HyWi0ffCUTdWxmRj0bUDFRnHHUOv8LZCvSSHkTi9F7tlPL73oDP74x1ZzaB70F4Qk6qwxpKLwtoOZyWDV+na3GATzg1QJJ2qG0ZyXjjOE50fWFJmyZarV5+h0yWDnlH5wHdfw3S4I0Ywo0NhSU0wVrTCdBJeX8yAHa/YXrxgd/MS7QeW5cDdh1+LRkQF5pBaroaow8mOYRwZu57ajRyfnshhYZkCKYHrN/TDJVb3GEZM8eQlMh3uqTWKuZs3YDIlzUSy5E63CbRqSdCzuu3DrAJjWFIhV0AVYg4UZcjrnquCbQlcTgFWU7GkAqlRT7d9J1GnvaT7TdMkVtdKY2xPioXv3n7AmC0xKt5/fOT+/j1bp+nNFu86VFUYVWVizxltNSgr3kmNK5+KQRlLv71lc/mS/aSJuQM9UipcXr8iLUdKyCgz02mF4OaJEu6xXJGXj0zHheM08/qTN4SQ+e//5f/MePMaZXqM60VU6HsuX77G6hdc3F7zq1/8jA8PD2wHy4XdyvPKctDcXV2z2V4RUyFEYRKt17+qYjcdghyONht5vrXBN8Y4lmXBNFU7QCmJ43F/OhBKcY2nhrDWNbnehQoclfy+rvcioCuNXbkulZGGknNGOdV+l6HWfDr5n+FyWU5LpGdsk4VrNaftZ1utESdXRwhLixjWp8cmMQH1FF1weXnJOIrvk8I0AVx9RmrJsodotFZrLfM8/y5lHvh9KKmmScVLBVOxrSDOSQp01/fC3KgVowdSWGRbX+uzE/mZsfS88BbiCZIQf6H/tPDTAm8ETz7vLrTWaGf/Twv+P3Z7/phO1hXrVCBfPDewdTxo+KtUffm73M9ZEFJyOTWodepZxz+llOTZngKGZGFPs+51Tk4E6+kl14J1jpyFd48y7RSSsdbju46UAvO8YIxn6C3eGuGaxwhGBFklV7LWxFpRJWN0h3ZbSLF90C3ZKHIJhAzVIDGFxmCqMLeM9oBB41DNvbWWgrMe7TxGKcIC2g0Yo9CqMhSxdLj7uJD0hu32io1/RY0POLVgnmamY2H0mv7qlrgsHPeBajuhIiqNHwfc+ILtzadcv/qM29efY9TIND0xJcPDh1/g1EQMe6wzwtzKYvfgDVitmY5HynyEXHH9hsurFwy7G6odWRbNMitSUMSpUoul7yzDRYcfDFN8Yt4fuNh6JD7JYJETZ0kJEY41Z0ydKLa2aaCJmIw7uegO/Qi1MgfxovHWYmxHiBnXdWineXx6pBt7UkkowFvNEjM5BiiGFBdi2HPY/4qH/URRmovL11xuLHfffc3+6YjTSkzrSHTO4ztNqZWYK7mK/UTIsiSvZovubwCDqSNUT4kyhVb1DSFUOl1QdRE7EpOx7NHpgbfvP3Isl9x+8ifMofKX//x/4OrFZ2S7oShLroZG/afbXWAN9Lsd49VLDo8H4pK4vLqhH3bMS8B0nuurG7TxFAI6l7ZGrILFU0hx4ePHDwzDyDhuqYhliVLrbtCIOlwZSk3kEihZCqxMEBrnhtNuM8Yo1hRZNB5QznCx1pSyTiDrVAFCvRfYKaXEohZyyXjXU4pQ3q21jX5cKFWUxtYKNb3rekBsWHKu+M4xTTN3d3upEVHyPLx3LeUtnx5r3/dcXV2d6knXdVjTsSyhQV5Le55yQJ/n+VRTn7M9/3O332NSEP/xQlva1NKWVGL/ao0+wT4yKsvYF1OmEhp1qi2JnxViayWMfe2k51i584n7dGouBRKCs65spVqhqQDPt1UsVL//e/TZI2n9fWuD+MeoW88bzbNfDayN52wxdbLkqM/VhPr0M0ppKartJwT2Um2BJGZv6y/PObfsDtW0HkbcT6lYbdDNp2ZZhN663V3i2i4eBVqLVLhkQ04Q0kQuieyEk6+KI9eROSwY69BVLgLbi+W0sQrvDKgOaxWHCRRtwsm1QWWlOUnK103RVPzJKr0bNhgN3RDZXRuUilRfJHS9TAR7Q+oC44sdm9trPr77TvYvPmOVpRbPMFyxe/E5u1dfsL35AdvrH1Bjh90eeX1cmJeZePyaylEeg1VShJyit464BPaPdwJtKs1muOXq8oJqHceYqFj6YcRtRtwLA3lCq4hWCyEEttc7MDPOgyoCqVklU15sTrgimSpkVXFagUgMCCFSGahKkXIF48TeOknDLzRHXKvZXW7E5TVN9EPP4XAkLEvL6ZAiPofA037m0x99wtMx8/bbO9589iNevv4RZb7n4WGipshgoFOFTadxWrfPlabrBowfiQXq4UAulae5Mtae6kZgg6JDm4guR8bdC6b9e8R6QbMZvEweOrJMHzH0XF+PFDKf//in3L76FDdsycXgXUfX9YSYWGKioFlSEwH2F1z3N6RQ2W23Yu+SwLmew35CW5nKfvsaUgqGseciSVSr1BHJR5fQ+9iIe4Lbp7w0uqnFOnsKsamVZxb6tPsRGHtVKD8nwpyiAtRKY5cf7PtedoBKnBuWICdxax1ilx2EwW11g4wk912pyHazxY2+5Sg45nlmmvfiPdXorcbsTnXImFUd3UnDbo9zjQleC/86JayvnSy1zy7Rz2vf/9Xt99ApOIw521tkRN7vfScn25SwTgRSMQa07qka5mkiNpuIFbV/rmEwxmCaif+aJrS+WTJRrJBT8/eoGaUbv70Vp5QrqpwVymf+u2JNZGt8pVNjWGXmcD7Vn4o1fO8NeX773hRSoahnRnwrk0mdTfWqVqdGsH44JFtancI8apWYPtucQo0VS2jbHGhXiX7OCW0kXGS/P1CKmHmNQ49WhZJyO6GKGLBkLX7wRlHTzJISeEPfD1h/AXbGiu0llRa5iSUliFHcW2tVbDcDqA4arbBiSakS4kQuMiYro7FKEYNCGU9R4vN0cfsJFzevOO7vKOmIdR0lTWxfQXf5hsutg94QbEGPTiwJjGXbX9JfvOLy9Rf01z/EDJdUf0WpFtTIix/8CXf377n7+oCuM9QZ11lIUFLk4eN75nlhniWroeTMcXrkN18G5gJTVGjd0XU7Brth6AbxRjIAM8Ykxl0HqkeTpCnUgi5CpnAZdIEGORNLJQLWKkIQPQm10vcDCs8cJ7RWFGvRvUSgOtfx2WefMy8TD0/3/OiPfsLT4z1+8ByeDsS0EJNGNeUuwLfffEtSG/7Fv/iXDJfXGGt4OD6SM2zHDR2ZEiPJKHx1QgvWlm644uLmDRjHbp549/4jd0+ZzaIo3UhmlB2Lh5o0V7ef8fDha2o54ofK7mpLOB5JCcIyo3WPNord9RUv3rxh2FxSlYMYMVR0zdTSjB6tolbx5KrF0Hc74rwG2lish1IUx+PMuDFCq45RdDvGCQqsZCK4vt6RS2EJE67tDCS7ODf6Z2JZZmIKVJXRRmGspvNyqBXmTwVk8tZa47xlWQwxLqfrdj2wLm2XZ609HdBW+r1z3fcKcUzhpCxea+ZqJCq7kLMJ6PF4wDnPNB0IYRKRqhJK62rDvSzLs8OzuOla608HWfndinlaTjUztkXz8wljnRD+8ItmbRvOvyamie1FSYKb2TayqFpx3pOzhKBUONm3FqVONrQrhGSMJpUiDBx9tqxY6arrhFGLKJ2VVqQcW+cTZ0BtRUW4Fm1xWG1+IifVdUGgmrMX+WlPUmnYYcKsNNS17tczhfV5p9Vt77GqpZ/rHtZFj9DqLFU1Zap2DV5SzTuFk5uiMYZUK7rW1gyavJ7za1AaPz6kzJe/+pJx0/PDH35GqQpjOqwH2gJXHEIjKIu2Pf0wkpaJlAO5dMKR7iPGKpYYcN4yL4EQE1/+6jdsNte8fv2ZvK8Ucp4oWXY2zmmGXuNsJeWJFANgUDiU9lSl0dbjet8sxQu27znu71imB5TpuXjZY3RGlUwpkctXW65fJvqu4zgvDJtrut0LzHBD7W8oZkMIFk1HQYG74JMf/hMO999Q8yPa9dRlxmhZ3KfmDul7hSqgqyKEI3mewFhhm2GZ5nuK8TwlOZHL0r1yedlxedWzvRixOrNMT6iccQ2SGy3EBYzX5KJJRZOUlfF/nSZCxOkdMReKdYSS8eMIIWF8TzeMPCwzT4dHtrsNxSjoHTYbRqXYPxVQkmJobWW3GViyuCMNm4EXL1/xuH9kt9vhvaaWiO8tVmkpxEoRY6GYSpgCOzOgbUc39ly/HDi+/chhhovdFsWWVLy4DliF7zTVbKlqA86T6MAZjtOBYhxz0rzYXPHmk8+IVfQ2UpA0NSoOcSIDvu8kklO3LPB+wJoOZw1xWSg1tj2UsBhjDCLmLE3NrxVLkKwDamoHUzmQpizNQYSjFqUSZWkLYc4FOOcsSuFmf68aPJna9wz9gPeW41Exz/PpOl8L/Fr4Y4ynycW5ruUkBGKMp5O7VuZUM0rJQmV1HYfDkVIK3ntSFmrq4Sg6AqWhIkQE54xoj0xLdGy1pu/7Rkw5k29iyMzTfPKTe/5vq7p5/e85HP8HawqqbfglTVyQGa0rReXTxptaicjyTWnh2ZdWzHKWhlFyPi2RpehbUg0n1k8pRaI8y3lnYI1pFhANjddK8lNbA1mL9/lPaQA8Zy+13w1nWOi3YSJ1ksetKmd1ahgrm0hYVDT46qzOXhXKqjWE9ftD69LS1VeKrT6xr0oWZ8r1hHLSW6yLdXkgzZBPTjelVC6ub7jYbhrzoGC1EjWs06S8CLdeW7SqONsxDh0MG46HPSEkTHV427PEBW06UWwTuL9/x7dv73n9estnbkRpQ8yRlNOpKQi4mvE+U+uMVjO1OqwG4z3KOLwXDFUZmSQ0YPwgVtNE/NjTOTlUGK3ZXlU0QvcdS8X3O5QbyXok6ZFKJ0BNw3qt37K5eMHV7afcH99iGLB1glCpNbZDi8YojSoKXRAbbGUkm7kWjLU4bfDaEJbEIUA/bohZXF7H7Ybd7gpN5MP7QA0Z6xSuBuZjxYrIFqU0qhpqqvTWoWIlhMQSA/vjEdc7qvVc31xxc/uCf/iHXxKqpqbCMR3R1nMshflpTyXTe49KGds5lIHeGJRRcFTkpWKd5QefvsbvBn7x65/x+PZnbEeNUfDDH76mppkYjg3pUASsmBpaT1UOZTyuMyj11OwTHBUPqpP7qaLiv3j5GWGfoVs45so07ZmyYhhuefXyx7z5/J8QiyHUzNt3v2ZeAj/+4gtUgTBH/LihNqaQ7Qy977BOnEkVIobNQbLDvdGs4tMSw8lJ/1TwEDh4hY2WZTqZ96UU23V7ngQktldxPE6EEOm7vgnBumcQsVzrpUjh754ZWj5nJv22gwOIfU5OReBUZUSsZs2Jzr5e/9LoMqvOYK1BXdehNSIuRPQ40tz0aTI5HidKyXg/niiupUimc1gCoE41A77vXP09zRjn5ffvcvv9mkIVWmppXP616+aUTgvaWp2ob5M0BttO3TklSkoUJUvKc7E+b99XGEdVyDUxTROliIGW1Y0fX8SUbLUrXj8I6wdkLdwrU2R9QU7P4/+kQazCt5rXwPYVugJhHIkd89oQ1mQ2eKaMLpz2GKfurNXpedUq3iq1loYfchpLS2MYyOM5TyalVIquGHj2/DQvX75G1cr93R0XFztqlUSuUispi51CxTTsVBaNvRsYN4ZJzcQls1RFwTVHUUsp8PLNF/zz3Wus8SjbNz+qhNBqRLBWyKS6kOKelCRsyNkNve+xXS8/p+Xiy0XEgLkUlphJVdP7QcZ679leXFBKy5itAin03lOxZCwVh6qemhXkQjVCdChZo3THdnfLx9qRilgvKJOoRJkgjXgPdcZBrqQohxTjHbVknNPsxoFN3zMdZvriePn6B1RVeXh8IGnNzZtPebh/h+43uI0nHO/pLjuyWTDaS7YxDq0dKkkymo4WkwIoOKTAqCzKdxTnSdpw9fI18xxIsdB5j7ZKHAKQgJZwmDAUMabzBl8LLggjSk+KKReUiczLE0+H9+yfvublVrMbR8aN4biXiE1lLVp7dHFYP8hnpBqUNrJjypX5MEEuOCvWKrUYtO2pWvPqsz9hOYyo9ECJC/PxjlllfvJH/x23b35MMRsej5FUIvvDHfunJ+4uerp+y/4QuDSONC8sKWO9b0lphlLXnZTCVgNK463sLpZFTuq2IQkxSRhNycLky3kNrlot6MXSXmvb4NsmJjPCVDSpoI1qtFFzgpKl3qw01zNkfSKvtEObUuu+cc0pqA1hkOl/9ULKLf1MmElSs0B2S8YIuwp1njrmeabvPaYdmqRo2wYL2VPTkpN+pO8dQsENpCT+Tis09NvEmXU6OFn1tFr7HAH5v7r9Hi6ppi1MQalM1bSFhyYqfaJsGQAtI69p4dgrqydV0Cm3JZtwvrNKQsFqo5IxZwfUNbii5MLQ942CJU6KQukyJyz/uQ2FmHnrk1f/8zd6ffPXF/D5i6lUw/rV93/f+n2/PVmsVsvf3zN8P19CrRNDCKcGZ5Rrgjt1wjVrbr+n6rPJlV5hLbk3o2TfUEoiLBFtYHd5KUvoLP5BKQdWuhtK7EhSjaQExXcyURiHcTKdxCDNe14q1vVicjbYtpPRxBSapbM8NlkypxYwJAlh3ko2gTOKzhuqaZz9nE5885QjIUSoSsJ6apaPnxZhmrUiIswNZqxKSwxlrOQ8o6rFWUNWEWxzrlWGYbjA+Q3TUwQSnaLhru3gkQoY1Tz3hf7Z77aUZcI4y9Unb9j2PdNvvhK2lY4o47h6+ZKYMt9+eGKeIqrfkssesxlJ6Uh3OVKqxVUD2mFMRwiVUhWuFOyyEONCdYbqDH4YUMbx8HSgG7aUOmNd48wYsTOJMXGc5LXtnGIzWLEmLxmlMhkYlGE7XGBMZB9mSp24uDBsusjFzrDER7SvvH75msMcmJMizEIaXEKgGzagLbnMaKVRJUMM9KOhBE3Rss+qSjNcvqTbOEh7Ssr4y4i1I5/+0V9Qdc/jMWB94unuHdSIdxU/WG5ur/B9xvieOMsyFdU0TLmy2nPUmsk1Q5W4U61a1sWactgglFIRvcciJnO2se1SSpRccXY4NRFF+9PIHi5qYfx1vm/F/Ewlh9U6v5BSIQQ5zK7UTq3zKcPgvPDWjfYqrqYhxPWyP9URGiNN4PH1WlTtei5tByJiQmsNq/+a77y8J1rEeeuEIqwiD4gf2KqHcs4JA2pZTnuQ564R645hhb3+8JPCM4qmaspxqqiDixXf8jUmUxc5YUcdMcWQkz2pQ6mVEiV4XJYzjbVTzoVU7AJkxFo73TzPQuvqbfv66r1UqUo45GLMJwlHa6DG88X12ilzo4g9Hw9rXd1Zvy9aW08Pvw05rbf11L9aD9ffmkBqKVS1TgucaLqi0hZ+85qnsP5+KXjiLX/SaWgNxpBCFJwe1RgThikExm4Uqq5K9H2H7wylWOapkkIiLIEUMkMn9uQxt5jHItTNw7SwNR00PYJ1EoGYkhLOePGiJldK4jarhpIwGrx3eOvw1mC0YskLqQisJfbGlRRFa4LW5JAYek/vB6ztKMoTSxGCp3HC2IlRfJ9CFDVxhm702J1EL+YWNqO0Zxi3hAfHckxok+msxWvFEiZiDBwLdE63k6rHb3aUfkQZRTI9i/GUfiRMBw7hiDIdVzeXzMeZfapcv3jDfPxIioUwzxjvGTdbStujoAxDfwHKtRB5x+PjPXf3d4QwC5cddeKTLyECEtyznupiyMSQSUHU1VUbfNfjHZAjJRdsBlNhd9GTykStsNl5NsOGLXu0WShV4/stm8sdyc3kudIpRVGWlDMb5yht/yb21Ynl+MTF1RthpnU9Sw64bqRURcoB13muX1wx9jtQPUl1pGKwfSXxJMBNLRwPD9zdvUfbnmG85ebFS3YJQhaTQOt6sb1dJ2st/83HmZwC3lvGvqcULdNnC7aXKig02pwyKbZMFt3R+XOi2AqxyAFOYGjRDwjsmmKmUhoVXJrSeqIOQRqMVrbtJ4Ua7yyngrrWgnWyNcY25bQ09BhDexzqVIiVUrhm7FlqYZ4XOTwbS0py/UoTKSca87Is0qDyueHIrqO2xD11Eg0757m/vzuZ5D1nHK21Q1hO9hTv+Z+7/R7W2WddgXxBbHerElijnZsptaKK2GErpclRlreGimkn+6AQE7HYhCnWUPNZsCFYlNyPmEFJjmoIC4WEUh21lma5IVhdheasKD9btXjSoC0reXQd4U5wYjuBrCyl9etKmZONd3vSp4J9hnfkn+SkIywK0M3GmLYYlr/3w4btsEVbxf6wl2WaUmgvxb2U1O63aRVOv1Osd0vDPrOqdN41NoHASClmlDEcp71gmUbR+Q6jxMvdmowbPLVkYphZYgFVmJdMjhnQfPj4kZgK1g74KnuJIk5lVBxUBzWDKm1UlKyJUntxTLWOhJXTZF7QxmBlC0/IswTHu57NzSXzNHOc9oL5GljiXhK4Vo6WspQM8zSjiuwZVF1IeaZOlrG/kXQwZei6gTL0XN9ekacN+yUT84RTFqc9dhxIZWCeI3MqZF3JMaDLgds3r+iHnoqkr1U9oL2i6g5lHI/7R7SxvH7ziexHesU4vuA3v/57tA5U46hJkQoIz1+K+CEmiIlYFKYbGZznsD8wT4F5igLT1QPWemGaGEdOCWM047jB2XaK1wldxRQOLVCLduC15/Lylt4PxLywsQpbVSN3FIqqPC1H9m/fY/wNw/YlevDMS+WwZPqU5HRaEr0pzMuR+ek9ih+D8VTbkbP495AizuwIy5GUHUo5Us0s4cDTFDDeM897bl9c07vE49N7qja4vsf1TjKctextQhbKeU4Cv8xBgu+d7TEb0w5LhZibeZ0ClMY6LRCDKeQYUdh2KK3UatDGYY2Rq0EZcV1okIZu8GzXNUfUFJmXqdUzObiWIhP2ehAztl2DjSm02WxxTibPaZoazFlO1/Zzk9CcRW0tTUUan/ed2GIn2QWsUDco5ikQVTkhIDGIlYz83pZIeCrmbfFcBV5yTliIzjn6YSDEtgdplWGZ56bgnk87l98xY+f3aQqmLRhlsVKF0iE4mTJiV9FghlIlXtFoWJotAMa1ggKoSgpScouSP2MS8zHVunx9dlo3zqI15KwpJbHMM87Zxv7J5BzPE0ZbEsvkIepr8SmiCV3awqWuQjVOCy5Z74pfvm6NotRzVsPKc67teVZyG0e17Doafl7a/Rht8X6gdz0GYUuV9j3PE+iAhr9nUM9iRMsKhMmjyrUw5dS+/xlspSrLcU9OUfjhS0A3AZo1Yzs1iftkVQVqwTkNdf3QwMXuUgp9EVFNbXsMrR3OyBI6l0C1kMkobVFqK3RXI423KokfJQZKzaQYmac9JYvtAzjRNnhH1VUIBrVSy55SEY+n4shRUeckGKupaBVwNlB1bsl4iKcVsqC7ur2ixltU3BIeDsRlxuqCsiPd5grl2mSjvUxMx4XrpElRMfQdqWa0yigsKLkIl3DElMR333xJSpl+cDztFVWNVN2htUfMDw0ZxdMho9qJzBhDSIpSLX0/oudMnCYpHKmwpIirFZRmM3ZYu8E3bv5y1IQlQYkcn45EA86D8YaxHxn0DZvdLVaN1OWIyZlaDRGHchJ6b+2GxBa7/ZRPvvivmYPm6eGBDx+/Yz+LM20OC4OrTOGB475Hu4IzA5NymM5Ra6QznoohTpE4B97uf83l1Y7DtDAvGRVE3X69u8H6jmG85Pr2Nf24JdfS7B2i7DGo5CR22OIeanDGQdUUJR5F6+I354zKpUGrItisJVHb4VMEr+0QWipFcyq0OVcRULafA4gxEFMghoXayAySaZxPMKP3noxYbAtELlPHMGzoup7DYd8Ks4h4VwGbqK5raz6OWu1pMpHpf01uQ66rIvVTWIvNXBTdKKeG2jI8JDrYI93xnHu/EnRkf1GaJmhkiIXj8Yg24rzb3HcEBjNrLPIfuCms48j6e088nVKaHfW6rReoxDWxhPdiCZ1MwObVK8QyqaOcgJvCNyXBy+rJ5E4KuzgZrhRS1V7Qs/211lo+bO0xcvIQr21clyJu1gVUk4WvMvRaWzNRK0S2JqvRCjttulipqY3HjGlNRKHI5KbsXvnDq1HVdnvRBCecBHpr0X8eVfqcxfTbojlhQD0P8+B0cayPWTWv+WEYWJZwOkWsmOlKZ11tr9fo1GEc2F3sZGqiSBpVSZjicZ3HYPDWE40npkRJWTQnSsJD0BrVPtAgaQgrBDY3XFRrzRJSw7GNsMlqZV6OGGZqDS3zoqPSs8wQg6LrBhSamgIVIRjk8CRmgVmhDbiux16+xPIFhHvuUiGWPcdF3o9RKfSwkT2OGZjrEacMtr/CdR6sQtVIVTN9N2Kskhxgo8AZchLltiqWEipWDxgtGgTxp3JQjWgYdE/fjXjfMc/vCaWisWyvXzKZx4aSKmpKDNst47iV16xkchW6txsAUyjJkKaF/TzTVbjcXeC7S+bYUU1PNR2xGkISjQ6hSEKedWjVgR5QdoMZLiXtbS7cvu54uHsvehWlyCmiVWE+3vPx3bfsPnuFygXnPQaFqZlqEnM6cv/tHV/+4m/50z/9KalqUvW8eP0ZV5c3cvAZKp//eMO4vSYXUemHkMQexXnGriOmFbopdJ2QB4S00ZyQlXyCrHUnSrecwqXYWGMblKqw3dlW53nWixRboXimFE5T+Rp/Ciu8E046gPXr5xxjdWIjdb7HaNOsaFKjhkoOu0wLZ5jZNG8wuY9zlnLJ6yJ6FZJZtK4nSuvKTJKaUM6+bHVNfVxZiVLrBP5u15zSknx3qnktCROhvRerKOWZW/TvcPvddQr/6G2lcmrJT1gVxEgxL7U+61COqjUlJ6wtTayhTm9+rYssS1M6N4FmbVtrPWH1z9XHrPfzjH71XH0o/z+3ot6st3XzOGpjoHB/G2VLcSrMUE4fkrO2gdOLfhLVtX/TSmG9iFWMde2DZcURtiRoy+YVJtMIy6qWIsI/Y0V9WqtsBdf9hDqrplVt6Fhd3WBlT6OAcRhPzeH+/p7tZtueF6zsKWE51Gb2JUrP0j6AOUdhVliJa8w5UkLBWdtC0S1Kd5IFUMApQ84KbTxURxtg2t6pNAhgwXWDFPui8a7He0+IgZim5kx7pJZ9W/JptNlQa49WPSVrYlaIx744v1buqTVSo6YYh9MK5zao7StuXv2EEmCyd+zvHzgsM8enAxeXAxfbK9ADXR0Yhq04g6pKrImqoWDonWHwjq9//cR2NyCZF4USA6TKZtOjEFxf20oqC7UKxNm7DVr3dMOG+/0ea3q6YYvtLdvNlli+oqZCDJHt9QUvXr3haX9kWeaTeWQpkYJQM7Wx6CLeNpHAwyHgciAxcqk7iulIOPZLxtVCXAJKge8t3hiMHbi4fkWqhqwd3e4abzWpQF0m8vGOD49PEm9bC/cf3nL9wyyhPc6iq0IhORLDxhOWhHOF+7u3KNPzxU/+gu3FFa67oGDBVTq3JWXFZhhwvpODQIZx06ON5TjvGcaxRfkqmgNMgzXO19lzKuW6TJYCKLCQFHl32h2sk7sspi05Jfn8NsNA6wy+Gxvzr7brYV0Oq1Z8aTsFCcZZD6rzMrMZN6eivC6RZSI8i3mB099dE/pqrVnmhZxrO/RWaTTPat96W3UQOUcq6+sgaIFASE5KQ1si59OflaXBRCEsQixJiRzjyTiw1nqqo7/L7ffaKSg4uX6KcKw58ykpiqgVqG/99hkrSbBz8WHRbYlmjD1t4ktTSa9KPIXYAssBYg2yaWNQo4v9Ng93vT1fFFfVoJ5aT5CL7ELOQjNYqafQyu5p6qmnD9D3nRVTzhglqXHiWmiFOaHE82Tt5FkVclpYdc2rFQhrAytSEHI9mw7KKWG1GwdOqu71CXLeQbQLIueMc5bDYeJwOOBdJ2pQvXK7zwlRwvk2WNux5KnR2sSnpSaBNVKpEBTRWlS/kRN+y83IRcyic1F41VOrOY22SkGpiZgWUpZoyr7foKrDWbE/MMuRcgzUEslJciZSjIRY6XvHdrxA6Z4UxKFVt+SyME2kfKTmyHysTFg6V9n2laG/4vL2c1TVPPbfknAc7j9gu54Xbz4DPA+PketXP2TotxhriWEmFYEMl1hY7u/4mBacVvzJH/0JT/snPny4gwxLDKiUOR7uWeZHXK9At90PBoNju7tle3EDxpPmCMrjNluU26G7K+ayYDee8eolurvBpKME+6hIrQFqQLuAqz3EhagXqt4Tw4H9cYI4YdwOmmK8GseSWlHL8r6mnFlqxJWM9Rti0WjXsx0vSGGhG6+pxtLxGpP23L/7mjlU4jJDCnSdqODFLNERVcIPHduLLT/56RdMhye6ruP1qxdk1ROLJO5ZL1OP64ws8/2A0qVR0y0pF8G9a6WzllQkt6A+u+7kVGxONMt1qVsbhVRbh3XNjHKN7axy0EwpijuX82JmGIVJpI2QO6bp2Cb22jyPIinK78kp4TsrNHfNCZ5JKUjEKStKIEXa+bPTgNShte7oNtVXaswYQ6txc7s+nUTztgPrSsNfIzfXmiMardyYaa5NV9JY1hz3lFbFcibEyLIsxBhZmuec5vtGof//9v60yZIsuRLEjt7FzN5z94jItaqyAHShgW6CjeH0kBwKP1Aowv9OEX4YigxHphvo6W3QaKy1ZWYs7u+Z3UX54ahes+cRmZUJYL6QYd2JqAh/bs+We3U5evSoah+B9e86frBTcIPyHNoIISCFAGgyg9ChbYdIXFfEZ6PmbFzdWhmBhohWmfq1Yp2DG+xFV6sV+EN3qlcaMJJfA7HB+l7/gYKGNZgoD6OSwCY6Ec540EDBvd5N837H+mFwkkKhfa8vdL59pBAxZ9Ide+NM5xBhfQcN0jGG8MjBUx9ZTc4hnqZpKMceO6Rb2yfKidVgpB+F/ACTroUI8PLFJ6Onoddu97x/vjUu3BQjYpqhvaLUYrBchSqQpwWnOyoxtt6sCU8RUkIK01jQKU2wRwaIoDY6jYaM6fTAaVV5gfjcZVDRMkjCtVT02qENqFuHKg3KPM9Yr2x0RC8QkzgQbIgSEHRD31Y8bUBNAUE50nROJ7z47HNI7KhomF/dI88nfPL5T/D2XYU8XQEsiOmejjF0zrEAcLms0HdvMcWOWlf817/4X4EgeDg/sLbRO+r6iFA26OWKulVI5IAXKA3e28ff4pu/i/j89/4IX3z1R/if/+w/I5xeIUxnbLXg1aef4auf/gIx36HHBZ/+9B7b9YLHy2vU7Q1SpHPo1zdo22uEnJEqKdhRBVuPXIN5QUNESDO6JPQWEDERyKwZ2xOwScVaOu7mM5DPCCGjFMV8fsDaCpaHT4DyJd68/gbl+sThLei4Py1YkVCtxiNdsNy9AvoVS1QsOWGZzxDAII8Z67UjLWfTJLJATRLyFJGnwAE2InjxELCVinlZmNUCQ+oFYC2A2WwyCrdgmlgrgNoAG4MfgwnYEQKyzu/WAJClCGEQp1Z9cGOqNijJawce1ccoaGgjSNwLu4KnRw7GoiUSpDQhRlJLQ4iYchjZAj8TrG7o0NAMkTIYTMcOZFc/8NGatRabBa3WpZ2wrpvR8yuvHdy/pRSU2uggehmMTm0dGgFYxkFO+w/LEoAf5RSYb7k4nb/o/SBcorUg2rg6qwQDNpmIvxutoBqgKDtsA0ENldxu7excbQ1lK4Q7TLq4dUVHR4rUSac+CnsbSg1DelY0mGdUFldBuhnrDLvjIM6nCBrRrYlll9r1WkIY8NA0TQg2/m67rragIprWwaRa1xXYCmKumOYTYswGYdn1HLIby4iH5rvXLOA1kEOKPBbSe2woNtGwsSYjp12+ozUl8y86Y8rlNgK1WMDnmkJGQ7VZGQ2nc8ZpIT6+bRe8ffca1+sF5/Md7s53rEmIUPBQdyfMTDBjOZ/4XK3gLjCM1P6TkCAhY8qvcC2KWgPu719hWV7hcmm4XDZ2zwuFF3tnE1COkSMyk6CvK6QFXN9V9OsGPRfkpDjfLeifv8ByWVBWwXYtuD89IH/xCteromwbGSutI9oGFgXK9YppFgrbacMyn3B3N+P+fI+333yN6+s36OsbzGHDnILBnBUpdhbB5S16SLh++zf4r28uCO2Ef/HH/xqnV1/if/6zf4vPfvrHOL38CYAFd/evEPKEvGxIp3dQvUD1CevlNf7+N99C1opzyqgtoGuCYIJqQMpnUjslI01nzKc7lG++xmm6QykVDROABcv8AqfTA0qpUN2g2nB5ojNDmPDm3Vv8xV/8LbbHgq0LUDsu1xWfPDxANwyjqTUgase0rHj77h2engpStsmAAEqvMHY6JAWIen0gj14XN0chRmzrhsvlgjRFpADbfxgwsEO/KVH00eEdVRJFmnAcbpJ9X9RazUkoaqWjoEIqMxNY1MzBORPyRKJAtfG5MVL/i86Ehj0mG3NpEj2td6Q8IZmW0aCOQ2zOOmmqUya5YNs21CsL2wJBzhNYCO+jMO2ZBaEmOovrdTPpG0EIMOeoluGwPuEweSkVdSi4WjA+MiyiNqzg7Lbjhxw/stC8QzFBiEtvZaWmSU5WNImjcQqqLOQFnyHQqPXfrSNaGHFXl3CIhVVzBZqw+7dsq0XlR40h6i7RMh60xyPHUQ69k23lg/FqvXQI0mhVd4NbSrO5Dxk4OITWuqW37LZMKY8JRjFmoLFT++mJKqS1V0iMyFOm9O28IIbEWKXpYG8RA90PVcXTEwdyAJ5FUEbEPuEftOI+C2l7E47XHZiRNGM9uGNho6DaBmFndnDdeYvsXKyLjrshiosLCuY54Xy3mIGGyV540UrGs+RzobEPwmhNQrBsUNEr76k2oGtACCf0RglglRUSMrZ1gmrElLmO5gm4rq/x+PiOGWNjQNG3K+r6hJgj6rqiyxO2t28x5Q3L1GAuDpCEermiXYEU75C7Aivhw7Y9QVBQ2xVh2yCtAT3i/v6Er376BX777TdYtyecp4D18g0CLkjyhBg3zBIhXZFTw5TJxJMpIE4nvNkUEiKWcI+70yf4+e//K2D+DBozqibk+R5h+gQhT2iyUc9JL0BdkJYZIf8S3/z617hIo0Q9JjRVNsgVweNjw/2LEyRe0TGhhxOKAlUbkM7oOGE6fYqUzoBTpRWYpxOiJLz+dsOvf/0WXz92hD6jtIgvP/sZQlrwdLkiLS9QG1BaQ4xnJMlIqsDLijnPmJcZWxNI00EWUWmQCBaPEYEQkSZ29XKmdUdMEZNmsn62Bo2g/HoIKGW1AqvPNDdj3kcaCgkJThxxjN4hF7dNQRh01KpG9sgG8dB4pzghxoyUgNxh3cFh1Ax9z7CeZNmk1TDEbB5AReJ5Scbe4zmSTIgp7RASbMsbg5B1yFsWI2Gfavaq3FzPXmcwhqMVrb0xrdY27LH63Ad7JjJMhqvNetH6dx8/2CnUysHqzYuylmZNecFWCr755jVevXo1HIKndDFFJCQWdiujdpGIaM7Co2KN5BxDrA2/bOQuC7+7tobo4yllVw4FvBisSCkihx2T9FSWL9tTU4qjYcxYdVE6WGs8sxkuDrJpvOjsdM0QAodXhIxtvaKUFQJFNmXTPE8jo+hdmc6ZNT82zAFW2O4d27qiHoS1BEJIC90m3llmgV3u18W0WCwG0EdFBLvM797pyIWtaJXOqfUKaAF6BXpHzHQqy5Qw5YgIRa8bOjbc3Z2wLLNxrZn1eabFORkBEizjaopS2O0OiVDlcKCY7T4AaDCKqQaqtp73vpMUOd+21wKRDa29Rm8GVxR2eObAsTxP336Den2HOVbM6Yp316/R6hvk1JHTPab0Eu/evMabNxecTy8gkpHThF4Ktu2CVq64XN7h7be/ha6PWD5/hfPpASJkn1yvj3gnFSk3dLkiYMV5bsgAljwjTRlNV85zPp3Q4gkvX/wEq/wEb79O+PrbJyy/eYPl/BkeTa47n16ihRmXK/VrqnLcbJwnhHTGZz/5Q7z79jd4evMa7VLQ2gUxdYQ5Y75fEKZ79HDCJ1+ccH75Jb5+94g36xPu7z/HVz//Q/SwYHn5GdbOvcegMSCnBGDCw6uMV68+wS/+8Bf4m7/6z1ivG/74X/0fMb/4CRoSDX8KQClolaq3Va7ocod8DmiiaOEEkYwOQjlOTpinBQIWRqnppSOT5LhL0sprpShcDAEpTOjajaAg1phKO5HSjBgS54JbYyNXuZFPhJASZTsqqsGtrQULcLiXUkqYl5P1SgUrOgsb2izyVqOzu/yFN8iGwEYx9ln0Mc0thIBt3UzheBp05FpJDSaJpUOwaxG5aqrbKK8FuBOgUQ9W0yijGY0UW3eIdRB03PZ5tgAoKagS0NpBCE8B0X9i9pEIxwYKDti23eR6ueDt27f4/PPPrUh8EIlDGMVa6gRFag6rmDfr7COIkY0bQvnoGhOwroRFlLIHvRO7nbBL2Y6mM7gUtQ6MPqU7vlh7uaKsbLRGLLiqaY4Y9NFKZdG0UwJZgjAqVc7ijbGZkSJ3PIWIaZ4RMxkPXZsVkjjLuleycOgYdLAKcHiGePY8vfPax/rtn3tfpfUohEXZYHeCx9kMjid2iBjU1agg2ltB71cEaZgzsCxpdN6KVLRyQYgJtVmPQ7fSRQfnR0scxSy1SXtbD4g9IoPNReSNAzFQKI/pNhCzQYgKSJwRoThNM3HXypE+cQHQV0DJSNsub5BUkKTjl7/5Ff79n/0bfPvrXyL2glPseHmOOE0NKRb0dkXdAO0Zl7WMyXbTNI8Rpb0VoDVs6wXlesH1aUMKGZ99KbheCnJe0DqfVQwdKTUsc8dpAoetx4aQM0K6gyDgbQ0I8RVqf8Dp06/wJ7/45wjnr9CXBVsHJE7YSsPTuuKcJjRSQtABpMC5Iq12nB++wB//7//PSL3h6dvf4re//Rv8zd/+BS5vO758+YB8/hTx9AovPr3H7//xf0MJk+sVn37yJT7/+R+iSaKuVbpDlUwxvCYoDcjLjLtXn6OVC9Jnn+LVlz/HWgpSvoPEO+TTC3SNiClhTglaI6IKkvCZlo3G8HEThC6IU0bKETnS/HJNkolYTXGgedEUwbJfg0taxePjI8q83cAbHs96H08MERKoioqDXANZfBEhC7zfQJXGvIQr0Bl9965YFqd4enAl8FkRrnGkZo8IH+8wb2sdU2YgyQCITaVNFRzDGa1eoHAGoDOTjsxJAKNm6MZ+zwZ2FVNXcXClU+2ABjlcTxufd4acQ+JmZYctIT21AxrH7/+u4wc7hWVZjDZ5Oz+5946HFy/w8PBASMgYOHt52CQkjI3DG+ANtUC6qDeiIESkAxzBCCMYZMLidKvFBsq3AVcMrL57/cCF5hK0J1wuQK0XdhMG69YUscgUkJQG1St0hSbDCqnNwTqqUNyqt44YG0qtXKyggSRkVZm7DPaTRRw2k5e4555peTH5qHTIAlNEDHvBm8b+tjANYGCw/u/MZNqgy/J74ljIrVrxTTskdKhu6PUKhIYwscmllwLtiuvlEaob6Zs5A8IOSmozhZEGC9iNTTkB9jP22lG0cRIZFDF05ClBbHO03tG6oCGwcH0+W+YRgB4Qg6CXzjxHG+b5JUQVb2tDv77Dt19/jX/3b/8t/uYv/hOkrGiXJyyiKHPCEjum2CBSR51FAutBvRf0krAZHIlGEkKqFXmreNoEb7/d8Ntfv8H9J6/Qy4Z/9af/B1zefY1f/81/xPmccZ8X1OsFee6cPRASXj9WrJhxevn7iHc/w+nVH2D55Pcxv/wZnlrGk/J+z3cnIJEq2bABQvon6ytAL4qYOFnu6zcV99OE86uv8L/72c/xe3/8L/Hbb9/h28eIdyXhJ198hktX/OQP/iW2FhG74O70Ek0TqkbMpwXxdEaFMtrOGaggnCkZMkWs9YKeEpbzjK0qIBM7jgXUjLIt3LQjn864T5/j7dsADQGqCZJOPK921G2DoAByRcozgmXbx3qTDgT1SHqouFzKmBbme2CHWXaIyBl3R70ygEabUBHlWVSVY0xbweV6QasNKScIkp3DB9/4gK19Xykr0VAQPaAMhgVysJnqjUGWz0kYEK3h/F4LpHMwzWZ7li617ZpFHvn7zAO3e6NmUIsRXHbyjtsQtzl7DdKeV3fto2YOiRrTx6Dy+44fVWgefPlnL0YbiykwOCFN1r08LqRzZjAc9+KCCaHCaWddnGqmkNgRkDEvATlXi9S3IR9d1w1b3yBBsMzzqA34Q92vjZnAPE8QEazXglqdr3/r3HwhQmmsRDgOkNRkLpiqLMS2zqlyxyJwjGx1pwPzAjDZPK02CG6PY0/F8UUzJV2hgHVET6M9fb8vc0C6Dwgqtdi/ATkEg+Ew7i9GUGsFHVHISNB2QauPqFhpgFtCqZXZT8xIoaHVKyBsOozThGVmxDSkgbcV1yslfimFPqE1jkwNElmj0YhSO6EqoaFXEJYIKUKScdCNYeL9LWW9oK1XbJfXePvmN7i8/Qavf/U3+I//y7/F17/8OyTtCBDMacIsgqQBfdvQAnCaMyjcyCwnaAVaR5QEoCOHwMzNpJZrVyzpjNIE3/zmNV59/gbLQ8LbN29wd0o4nTJe/+Yd7rMiTUBaAM1ADwEpvMTd/T/DJ1/9KUr4FHL+CbZwxmNPKCFhXs6IqznIFE2okY44p4guirptuDw94uXDA2VRXn6KSQSlrPjtmwvidMarLz/Dy/gS890XKJoQJSCfMz7/6o8QOnCa73G9NLQOnB7uUUOHtoqtd8xGne4NKNZMlacT0AqaZJReLXDjzA9oJ3POIIcOIE4zTg+foqlCZKLIIALadjUop+PpesE5RcwhwQfVO9TosGg0xlGTDdo5tGZbV/bN9A7BXjPsnV3QYt3N4g2rDrV0w9OtpkIjHWnwU8Dd3QQd0vfucDB+fzfCOz2dqENEiglb2Qzr7yMgZiTvWRF1juZ5xt35DpfrdcxDZhBM7SNBN4Yfh4lt23bICDinXq0W6N3QrVIWxFGC5+jC6MMaVw1+VjmLmq2qGEXnH3r8qOa12hrCgfLp6R2H01vBN/HBVh3xP5T1TPg82xHFCkdLplTROh9OFXYlRAnQ6B16e9qEkJi61YLNOjOniRhfN5wxxYwgAa0Tg08pGCun4XolhhdNTpfdiWU4r94E2gpabdQOSgnRjFbvVAJsrbIL0mRwuTgjRCZyoyGIkVpMjv212pA0DlGq1mzGgGUi2sMu8QF2vgLAuvU9jY6Uwu6dhenRCSmKFirEhhDFGIGUuDgKZ9WGFKC9IUCxXd9hDg1RV7T2DiFu0NZwvdj0qnYH4ITrCqgUnCRDQ8YcElI6QYRFfaiiqmCCC4glSEyABLRaEUxgcLBQ1OfdNhpqkxtYn/isg3jvRkUrG9rlLdrTG/ynP/+f8O/+zf8Ivb7D5evfIEExbVe0dxdECTgvd6il4KlWzCb2tsSEHIEoFYTOQEcvheFqJ+tNpTMSThGxkrteHisev/4lAmb85X/4FZZTR6uvobLigo7TXQYmwVYLLusF88PnePnlz5DPn6LpSzxtEZpn9JXSJFUrAkg4uFyuuK4V06nh8y++QAoBW2fGtl2/xRo3PL35Gpe330LnBdtW8PbxiruXr/Dpiy9x//JLIJ5RmgJNEOuMh4cvCc2IIE6UcI45I2hHCgmlVKSQgcCxlNoqYpqM1CGoXZCmmRk0OtBpUGGBjQT2rZDVPaHWgnlOqBubLvO0oJmUDLQhSUav1MeKZoCZtVP/DANq6QhJgdZQehsS7gIlvNuUdYbQMYUJvdt8DAXEhmJBaSss2kDbrGs6ZErESEDIuyMSKAcw9WrFLQteRKEQhGikicDuUIkF08QGsVorZ3DkhOt6ZS0vBmytIumEmDP69YKqDbMpObSNjKleGvWvbJBVqU4xZSFQO+XAGcRtBtPbc6gupyPWWEd7GITOGkpRSwlGN1cgmVxOtR4Wh83+yZ0CcCu34KlPjhE551EM7e40YFSi0aUAiwYtHRvTxXyYTkIQwaYKNQpirYplWZBzwratHC7RPVNpWDfq7CwysUBkDoqRtheyjR2QBSc3pPAWe8HpdEI3/FGbDejAro8eKg12ShERJtgnHa0xRWT9gsCXKzg61l47v/t0PmGzotKezZD1o6rowoUbQkAX15cKgGVHrurqUNPDw8OQzi11Q0gLBw95/SKwZqCtIoBF/lZXyo6Xd2hxQy2P6P2ClLmo0pTZKZoSEDIHssiEy7oha4YiY5EZKdLQgCYEKgEpcoi4IgABEMNan0c23jzUSx+O2Iv63BAFWi+QckV9eo3Xv/wr/Jf/8G/w7jd/h1BWyHZFCqSjnt2pb5W1kNbRUVHrhiUrpsTaUAjBRrQaVmzUPatSkiFVBal0VBu3+fb1N0jzC2y1oSGidkXvCT0k1Brw2csXyOh496bhm3fAk7zGcv0G4bQgnh5QCzvCQ54QekfKEQqy1uZl4szkkFGuV4pH5oyLCF5/8xuUyztcnx5xuVyQl3v87A/+EC8/+wlUMjoyfM5GUAU0Ys4LAPZ15JwgkUXXbF2w0gNimuFDoXKkweutQ3sYMMgO2RrMYx31wWaC1ArUUsi8M+2waIGT2wau4VvZllab9fD0wZOLiNCQgFat8ZW9SSJWPA0wzTJeTC3sa6plVxcI/D83QTB7EwAB5w+oMX7ULZDNQAnmxHpnEEpx5QOsa1lBjAJEIKYJu/gd76G2egjMgHdP77CVjbbQ+g9ab6grM6H1esW6bqOG0J3BNyA1DPhHsbOMjhR6J804EuFF7NJ9vv0+d4U1CZ4rjSa43338KPjIKV87RmhYXNijfzeGjpfsRR2TrDjgWgoQEkIavQHUNyI1kprjYpIMGRJ5rhQSWksoZUXZSD3tvWKeMpZ5MjYEv4Ae3xkLHZITi8jCgRVimUZrZD2w9dyci91LbQI0QDUB2GsjwaLd47OI1tjmzSx6UHKtvhi6Dd62jfy8xiAME24Kbp4y+4PNOWNZFmwbB7zP0xlPj5zrG6sCekUvT+htQ0oK1Aa0zYzhFVt7QseKEIHSG6JGJJmhOAFyhxDvIOGEECYUZabVO1ArnSzb6pkms5BHSmbvAsRwEzj4PQCO0frwj4qyXaxBLRhtrqLXDdvTG0h5sjnLK6ZlQdOGoDMgHb0EoHMD9NqwrSukN4RIx3O9XhGgiItgXhKmKSJGFt3URAu5Ce05N2YTIQIhRTxeK5ZNME13eHH3Oa7vvkWTFTrfo0LxZqO43+nVPc7TK2j8BCsWSO04i0C1IeWIpgW1XHD/8AmdbJrYMIhg2v0LcgJeP71DV8F1LUgx49VnP8G0nLCcH3D/8hVUJjQTjxMNQ+RM0bHVgvO8QFLCVllLEWGRnxpXLsliEK3tIxp76yMw6JdMGDZ1pSnf1KxulJIP9a3jf8d9P97/IUB1XJt054yuFUEzona00rGum+2DztqEeEMZ30+1OeQhyNjnIUTbrTIYRKpHu8Q9SEen9v27XE7vpMu+dy/AYAv5dTt1tGsb/+ZNadfrFaqUsijWZXy9XtFKRdm2wTTaGUG7NM/zwrAHUMd95FR7DxJDsJHI6hIbu2yGQjmbBJSumZZ5b8j9HcePdgqKHToa/QGHRTAWhEFGOHzeniDkgKXTuO7fE2PGPAtyTijbipzzgBxCOGNKM3qpZIykgJIDLpdH9jPUDa0WnE4nct/hkY4lLOKLKJBaGsmM6miQAOQpgsPqHeawzWBTnHxARkoZsYPNOgKgsaGOmu8RoiyES/CiVr+Z/apKHaTo0Y6/dFWr7e2UOx9KThit2QIMVsegs5vzhNQ7Uqno5couXdmA9ghR4r29bdYEGKCxokpFlIRW2SimErCcX2BePkGe75HyPURmqCTMifDPtlY8rRvCWnG9POJyebQU16ppnRTANnjfYq88YM+OMCi9vUcsCUAL5LIL0CoHsfD9NTy8+gRf/eIPgVqg5Yrf/O1fo14uWPKEuq4IQXBOGeH6hIAOXZ+wPa7YaoNcuqnkCqaJc4ujKCmOraFV5WxlbdCgyEvA5WnD5akhxYSfv/gpXv3sZ9A846uf/ilevvqEtE4Bfvn3f4u1bnj1yRcI0z0KMnK8w3z/CggzytsniFCIccpU27xsiut6Re4J05whgSwfCR3r1hCnE159ljHniGWeISkiTWdITNBOQ1AbKYvRApK6bdhsGlhKaUSYpVRct5V6/xqQkutkieHnHogY8iwAVFCFGfJWG2pXq8dh4O9uWLyJdd/DVmfCLo65BwTBMvi9fjfqKykjiyDPE9KW8PbtW5sAtxF3RsVpuefUuWChnSqL9ZY1OAwcbA5CjAkCcvhdl82L3gi0R8mGhjlWT4bT+/DKYAAdjHNrPLdPURvSNxbw7H1S23AK7cA4eu9c7hAGq2gfcexEE9rGfdqa29TRqX3Yb7wWZkQ5k97NIT0/7PjR8NHxZR83uh9MBS3KlpsgYWQLOMAKIQSTsNVRlxDJUJvy1bsPoKY+j8aILmFMaZo65TW2LaDVMqYQUaF1b7s/fh8jfXZBdmv6YIMKJ4DV2tC1Wl+BANb44o0jzfTXj3NbXdyubCYhrmqjPfnyrpcrfIiPP8fnz9Vzd1WMLm9VZhOtiEVKBSknVO1YjT2B1rBdnlDWR4is0L5BwoqcN0BX1HJBKSumfEYKAS1SGz+mjJgT4iQ4nz7B6e4T5HyHGE9QTOiaAA2IIaMZ3NI7h3+oFbN6Lcg5YQ2Rs5DzTPlssU5PGGsDhNIIIepI0adE/Lk0G7uYmJ73mpBOCz777FN8/vkXaOWKcnnCb776fTy9oTRDeXrk8B4B3r55i8c330JnMlCwPaL3DU06rkURbVJdmLrhxhRQQwVap9ZPbZ21oAbEOKPKHeLyE+T7l/jk53+A5e4B21ZxOp3w+fILpBRw9+IBl7XgzSMH1Yf5HtO0oMuMshVAgVaBp8uGtDzgxYs88HsFkFMEtOLu/gWeHgXLFCHSUQUIkjHlE0LKEA3Wb9AHNh9DQNViQccKyIqcMroKni7M4jiFTuBUb8+BvaiqBugwY46Y5zPfw6B4ckysr1HPkn0vPcepd9LGMSAgndkNKABrUOvWt8TxmVPuWOaKWojfpxRNdnoDesBW6Kg8yzx+J2uV1jwpSlTJAg3RaMVuAEaIIaSkAzKScKTR70cMERpvI/Zd5JOB26CX1mp9C7toXSnFWJP7NDToPmvlKFh3DJSdmh4Nmvdn6vCrS2WQsMN6hH+n2yX/fSo+7COIf9fx4+GjQwp5dAg3i+MATDoE8zyTEAvfVdWwQ3PjpkHUOxBiH2wDesmNRbsYkWXhx5vgdOJmLiWibBtarbheruhNMc0UZXu+eKcpo1YZL5TXZeUCgQlueQTfKemNOCIymEyERxK+QUII2MqucyIihylvOiKuY+/BEX/ls1K03mxcJ7s6u+GXvXcgcnZtb5URNxRS3yLEFTE1IFwhsiJPDTBxOsWKNN9hihlVFqicMU33yPmMIAtCWBjhtoytkeYroQMRyJHzLWKaIJI5ijNHrNd3WMsVgoReC7Z+QYZgbR0xJ+QB9zK6DQblEffk30/TjN5npFrJ2ws2r7k1TAFArXh4+RnQCi5P7zC/+gxLJuOlXp6QQ8CcJ5R1xTe//Q3+y3/4c/zlf/hz9HXBEhqCXlH6BddWELcNLrYZg5JaFji8JiNi29gAKU0xnV4gnV5hefkzLK++gCxfok93HE8ZIu7OVAF+u62IS8DL+4B1W9nVn8+IOeH1629wWs6oNWArwPnlGefzBBldr91UchvuHz7B0+UCsYiutoq70wv0wPrOPJ/YOKgF0GqkDp9LTtZN6x3rVsxwZZzPd3jx4hOUSnG41vswanAKsD7L5K1ASZkYxVq2gaF7hKpm1I400tFl28oYDLPDoRwxeVzrIoLSOJo3BgFaI2NGAnxWgIiglg1PreFyuSLmEzinuY37cEE51qVcNjsCaJSsBgu2CNYjpdblL2qsJkHAbaD23F4dA7lx/4c5yBTYs/GZh4zAs/xhP9WRAofRPxRgdxaLDWJNKQ20xL/jaCv8P39mR3jOa53QXfnghxw/bhwn8Myo8cuHcT8afheCOzxUpzF6UUV8U5pxDCFCQ4Nrf0zTMryfhI6UJvRWCBX1DklAb0ZTiwFpmhDiim1d0VrH26cLphJwWiiy5t/vL9MfNOC6J8QkJQJReU7SwhrgzWedzixFDgrvtaFKGQ0sFUwVm51TAJRts/b4A87KJXHzmrrqGCLDVJwdv4BHQ2JNPGU8UwppKSQ0pKDoUe1aJlQtyDFhOk2YTgExLYj5hJgeMKUXCGGB9oTWI2qL1lwWhuJmjAXSya4IkZ2uHCUYoI33d8WOd25tg8QJij1q8c17fPf+vGOMLMQKnS4sYlShLEAv5Jp3kKES4gkxn9BFcXoJ9OuKsl2hMeH8KuGTr/4Qv/fP/yV+/4/+BH/x7/8Mf/eX/xFa3uFuucMf/uGX+Lu//DOs7YoQTRgNHZCIkGho5mnGtVRMU8Q03+Hnf/BH+OKrX6DPD1jDhLg8IM+LiQNGiCSs2zv00sDWlgVpOaG2gJRP+PKnnyAiQkLGq89/AskZKk6NFKqIloI0LagFePnpl7heHhFTwLLck0EjEdoDtqIIcUaeEkItiEGQYoBOM7oyO3WoMuUJ9y8+Rc4TuhKSDVEGRz7A5wzv9QIvxh6JAYgB0zxhW6/DSHmAts/qwI2h353GbqB8CqNrGnlwo40MIwkBU56pu9UbFNQB6x1IacblckFMCokTJLDTnAZXsSwTSmnDQe1QS4Aaa0c1jPUICKY0saGGF4/aOwvfujeIDkeA/V7cXtRC9QGvQfp/7gCOdFOvPRrFaPRD7XbVbUA/QHR0Dr1TCy5GsRqIjpGfB8sMBpdEL1yKR4zpGUMcPVtkLv3u48fBR3KbEXgqkw5FUt6oYcj2UO3/P8MZrRAEkCo5sg5GpNEmJ3mRCGhkCgRFnljICzWga4bESKywRlDHP0JqhWJFbQXvHi9Yt4rz+YRlWRBTQCkbIRHnJgcW7WKI9hIj2QHRsEplkdX7A/gABK1UpBiR5mlEBl53caYQZ1NTRnjvxGYGItgLVV4O90JzkP2ZjSL/zQsxbFgC0nKH8ykjJYGCQ2kEijlHcB4wtflbE5Se0OuCqhG1wbSIOiTUkUpHAVQbmUwSINLQC5vQokRysY0LXltDrcqu5VLRA6mIgHO196jHjYKnv1up6EMThu/OOeqqASrMWhQNAKfAoRcKEMaAPk1AzmgxYlVFfrHgT/77z/HyM9YCXv/2b7E9/Rb//i9/hfqu4dXZ9JwSZQ1U6IxiDpBGnac1RFy3DU/XgjSfUPOCeTqjKt/JfHdGbQG1AnE5o20rinbORVBOyYtpwpQW5LQQKoqZ0mjqGwlAt4wZgMSMNC2Ipn0TEw16txnmdNIAO8qijV9URLFagpDiPc1Urg0x7UbesvbjlD/XwBpZOyM+wEq2sH/vnfIiT09PmGdKPSzLggAZyp7AocNebiNYhwlz4vCZY7DQmikXq2BbK9a1QjXg/u7FMHJxEkST3A5m2GJMOJ/JIBQJOJ/uEUJEVw67qrVBUCEh0vFYZgUzkhAYZA2T18Dhmhp8yM2RPu5YfinlPafgv+NQkMtW7MZfb9lG9hwMgRqEErW6giMIfj3OWPTvONYOWuvWN0JEIuVsdHGwK32aORLURg78kONH1hQOUYQde4ooN58aDAWjnHZDL29YCbozE3bJhj0DaZUaHm4wWmvoEiAJ0F64EbpTRAWpN6RcENOGWjZcY4K2DXVbsZUKfbqiq2CaSNXjmhRSYSMjE+0KGF6rPSAlHVTZ63WjRlEjRsiCFjVGuhWDj0UgiFoRXdAK8c3jZonPnSwfDPdw7+igBMfIKyzlHbRgFfTgqqMnIC4IObEeQY9CyKCL1UPAP5VRYe3dtNsDNAjQvRtT0APZWlBO81LtqGtFKRFP2vD3f/dX6Lrik1cvWSRuijBHrNsGDYJJ5pvajjsGv9+ybZiXBWmayciJkTzz1iBRyeJqsBnVAkiEmrFMAYjaTPa7IeUJAQG9FDIuyobP/+Bf4P/++RfYrt/ib//L/4K//1//DL/+Lw1vL7/h7Ocl2jxeU8kNijQJclHo1vDm9Wv88le/xO9vFZJZQC+1oV85GwNhgkhEmjh9TpXaV6JidZgJIc1QySjguhL1Tl2uCbWUnvOBSRsNqRoziOMpg7FoXIiNMAkAUZ/FxPcDhSACktA60Ho19o072J3ayJqYCR5aNzULtaxbdIN/AwJCilivZTiE+7t7iAieHh8xz+Tir+s6/re/a9/jMUbM04QgkcOVbmYlcL9cHp8AAaY8YV4WC6BoNWKcMJ/4nE6nO2MaBZOvTqMRDAiU1JZuHcLU3hIJripPSTPcBqaRNz9UB7xn6blDOHYiNyO0HLMBd3bV6gcxxd2Aq4wM4ZhJucinMzSNX/IenHx0OP5cx3d2SgOlnJDzjClPQyaH0NN8q6f2A44fBx/J7d+Pf7qhIyfcOwP3I+q4Z96wuckggeMV7ePHKMMfTrQoKAQrSIsLXgHaK6NSE+ELMUNiRi3U2mnbFQGU1C6FeutO6yLMz9GBvRnmGqihHoNRGEVssQUsCyONYumhp9PX6xN6r3wZoiiNLeutcwAIi+kdeT4NDDIEGuJ0iLSAQ8BG/iA0MN8SAQKI89IJcrymk1RDjGhPG+LaMaVMtcauBnexYN+tJ4IZHwv8XRUIJg9iEWQz57jHFfvoQWhDqSvevn2Lr7/5JS6XJ3z11e/hxctX0E4ZgGnKyDmN9v1eKGMgvaNZNlWKNZFJYCeyKqNMKKQB3aQDRLEHExroJJwxEieELJQnTxOe3r6DakNrQI4JuBOcX73E780LHl68wPXdW7z5u4KqTxw5atIckAZFg2ADtW8SpCnKSgbMdi0o2wVpWaC94lKvyDPnXasCOdJAAZQSUQRozECM6BBuXBMw5Oxvq6sEZhWjj0cCnToCcp5tCliCj071daGxAToDIIUTVmeDCkrl4PicJ7Tmz9Tx626A9g4Bd3RoZaE2xoR2qHO11sZ1U8q64c3bNwgh4OHufkCxHFFpdYK6PYNHbB4y9jGTqorL5bKzelSwzAuWZWEmLhHTfIKI9x3wfub5hCAJLpYnYACUojfSCiQDKbERDtgzkyDR5FqO9VDef2t1BJ3ejez71P8cDqFxJnzbNvQDc+ioYRQjZ6y00PD09ISCHWEYiIoHQOJqxAa3h4A65rDLyKqPDuHIWmJ2QEeQ04R5XjCZhhj/S4ghf5AU9F3Hj2YfPS/Y0kjLQe/Hrfte2PAMtllXHszBhPFydEAo/tDUoyqDTsSoZApFAyWtoyigkVIJxRQJY4XEjJgbYsrQKWOL1CfZthW1NpTyhNPpZNIU1iNhGb0bHo3BZHj3YrF3OsYg2DYgwKOEilKcPUWsVwJZRD4CE2C04EUfMi+YERxfFo0GQPfJJiWPLI/pqB8jQikFoqx/VBPvU4s6mDRwkDoIbiBKMkwdUGnYO+FZZKTBYARDoUDynaHs/Xj16gXyBItiM+4eHhDDQgVSE9ADgMd37wZ/22sLIoLz+czzdUUtG0Jn0U9soxIaoEMIxpxhB3yEpz2UQo5mdBUSE66PG/rWWduJCT0K8ovP8dWcUV5/jX/39Bb6+CsoLui6IaEjhG5RfMWUBakAoTSUdUUrHcvphCAZMRJ6FCYYaEoDHEPkPIoOhMh3HWKABqBpwdYKIiJmmc2QNs64nvLYKs5GUQSEmDAlZiLBstGq/j7BXgV09OazEsCsRwRBnSqekKc0IkoJXJ+UjeCa6+ijXwMigwEGc8KEPCpma5JsreFXv/oVvvnmG/zxP/8jvHr1irUY4+Uf2Ttu/D1DpC/S4UjcyJ5PZ9zdPVDqYrNgSfeahkhAzII0TaNpLsYAqCuwcjrZCCSVgVayXoxSC0Qqcp4RQ8RaVnQtnJeQ0gHu2pk7R+jnKEw3apG1oN1kPHux+Wgjp2nCu3fvKCjpZrHvAbD/XbGrT+shk/HmT/9+3+9uZ1NKmOZ5OIUUJ6OgJnjN1mEw33s/5PgHzGi2iNEOMYcAIRuj12YbWUeh2Y8Qg+FaPo/VOM7GCui9G+PGRaqswcAtlphgVReICqJQXVV7RJRoHrSaQeyoaQLawlQzLyhlQ9lWrOsVl8uKlKwbObKOEEOyxjN+n5gWeZU60roYI8IyIU+JAz2u20jLt22ldHZ2OMoF6Gjgtu0CMYoYG4+sycvGmLrz9OcDBQX5VC0bSIeN5/ADs4e6XpF7BqUKGmJg9BgDM7GuFdobN5QIBx35G7Xko5lwXhLX4Ccj7CaL6SwMPty/wsPLVzjf3SGlDCAhxozamxlp4LQsmObZmn0In6ATUlNVbOuKkDhFr9Z+Y0iCdRongzigakwRW18xIQYOXil1hUphA5uCHdkSkCbWoEKiAu4//9P/C+aU8e//P/9P1Hd/C8E7ROE4zil1lALECMTYIKGgtRUQxbTcQWVBVUoqxAwU07j3gq0XemE4tXbYWMtuhAEGMRy+XtBEBmUQEFQAooopT0imPnsEZIPBHAFC+RdzEinOCHGy4fPMQjxC9CIpjDpJZVxi1oojfZS1AIj/7k5jlMBoPOaEFhp+7/d+H4+PT/jrv/5rfPHFFwPXjjFZU5lltiGYxn8nkw6BvP11NVHJiPuHB+Q0cSZB7wdZ62BLn+KaIahBchRlDEIIqVQbhxoiTPCBkF5yAyhI08JgTQKp1AjDTjU3yJVFby8Ue0YdhOzE3hjpj9qBdSqbSQJrACYhL4rLdUPvDXd3Z6oyNGNhGrPSazejw753ikOaUxDBuAd3kCPjCS60F7EsM/I0IyZmCT7wy/c04PI4weTA/4nhI2hj8crHcsKHTXhqa5G9tZ2zgLN35aqyLV8cFjLMmy+SPOYoEa6XNDSSQgDiTt8LStiI5dxgRTgWKFUVTRJS6Gi9IoA9DbJESJwR1gtCyEhpRopPWLcr9dQrjdBsQ3Fa75aiUh5712tixhJAp6UaMJ8WS3M7xNrIZWQ/sK1tkW8Aemcrf4yW9fQG6eSmBwQ0R3Fg85xt6lCIATnxJW+VomdQRvMBgoSIXhUqDSGwEU57Qx8ZHHXnRdn8JsHlfjt8gnrQaM7HjFvvvM5D1ERjERDCjBATWuXIwK10KIpFXxG1NlyubIrL84lDVJQGWBWjvtBrRcqEPdxQDVpdCBxqlIMVaNuoHxHjp/wyi/sN8zSxXtQZRUtoyEmAwGcWlwU/+5P/E55aw1/82f8LT2//GkDHooooDef7jPJ4RVcKlz0+/hrQjZlpTOgquLSCuzkZdg5oD2gKaoKJWi9AGEyyEARTniBqkh7KvSSISKbpHxAQOms5eZ5G161YcyKhLVhUC3AFUmxwOTH4iCYZ062HgYaEtGbi2RVsLCNk6NGkD6kvpY69jNiRJCEJa0kC8vVTnBBDxr/6kz+FCAwqXG7xbqsgijnx3nRIv5RaEGLC6TybLA7ZfQjR9P9NksHWLEG1iBAD5mlBBIM3VUUtZClS68sgOJOFod0xJQNLr0gq4XyG69M71FqQc9yvvXUOTTKZ7lKorXbTXGYzZVR1aLV5NN+1cQQvMWnUuuJ6IU2294pm4o0jC5AwYD3Yu/LaHfun6EzX6zYQlBAMVrTo/3w+wwcPuUPwAI4oiA88YxDxv1FN4ZghAB7L7A0fOuAh/m3/3RBIP/1gkVphcMvuJNw7Hrth9849INgC4vd7x28HYkRURWgJIRT0GFDdcdn5ep+oAb9m1LJhu17hA8Zj3r11jAHZ4CU1hVQvFO7Xut8jvbscvDU3NPWNaMg8GmH6Tr2XlOLIWDxa8uhDJHBMiILwULOioRXJo3dNG14sctugcnymo5ciur6T4cWm/e4S4Mf6UO/cLIbf8fkbBbg3W/Sho0mDSLEomU7VtXYk0NG0xixidIqPSMgyhEMtaZqYEqOTtomD9tMowoGDebprw4DFRcJ8nL1bK+sCLcy49IL57jP8y3/9f8WLFyf85q/+HH/9n/5Hc3ANKVWc7oBPpSKcE7q+wfrmv+LlFz9FijOizCj23J3eR0jUHo+xdSQE5HCQFxdh8bTQOIeQEGyY/ZGuTccNkwmxdQUrRptDUdW9NjT2ig+dYlQ96OIhooWKUVx1YoNy/m9K/JxPMWPWH9Fax7paHcj+n68lvpvZDI6MXT6yDnun2mFqnc2miUXM82lMWvNOXIdB/PwOk/j68J/BomiACEFjKwLYL+H9L/anPbMDyjqeJe1MtybXHZ6R1i0j2JlGvlePXcjAoSfDnMbREPdDBjFo5AbrHfcjWZlkICKQ2g543ZU1yCAsqK/rlXsBhMo4upf0cX9W/uwczeAS2Gs4/5vAR7e9BrAoYzfwjnfyM4R3LHHdnYK87yhExPSBdizuyJ++5R7vswXC+N+O4Vq/gm8W85DJimQpJeSU2IXbG/o0YZsmlI1zlqtRVMu6kgNufQUctMPFXVaO98yZjoKZ4O7o6AzUahRi2dFu8FLi5CjnO5NiqqjVFkqngfWXyjwrjhpMrRUkR/H7qznIHDlv2GeZ9oMM7zAegRE/C2qsV+SJfO3ug3jU6yreQNjHCwsuVGbRPqMjnyFhmz96/4Q13UWDtgqbbqIEKxweO15ZSFtrwTTPeHh4wDRNY+zp47t32B73ouROq8ToQg3Gzwdw8+e6Nazbyg3YGpIKikacp1f46o/+Nb782VfoMWN99xtcXv89JvkW59OCfC746XLGtWeEy18iXX6KkAR5+QJbmlH6DjFyNsheCAeY4VDt02dxBHbkmrQ5Md5sz9Oet0ejI/Cgc6tahxPdSR07tnxk/yl2Mkg/vKeYKL/eWkXrx2fv9TvBPFNaWxBHt3GtZEJxgD0GjElZCXMIwmyDwQSzEhpL1waLOC0zcp6H8x+ognjDp+2VECHSRiMWIOO7VDvpymbwnQI7gjTzA3vAdqTG+iAuBYIVZ2O0LmNCQtKpHLCVwgZYp4zCht70jiBiKEIZcI72fRbKgM7sGq7X68Dzj3RSsXPC7CPnu4QxlKg2SvF/++YNlmVBrQ1AxTRl3oLJeodnvSXP6w0jiznQgH/I8aMyBTfU7+lXwOePmlF3vOwQUR/Pc/zfIkLYRW8/6wv+Fkvbo0t/CFw80UYrW/exLYYYM5veNCIGNnCUsnHi1jwjTxm1zJjmGdfLBdf1apz7glKrjS9UzBMVBtfOjs3WK9UosQ/Y8MiaqeVthzNAnx5iwJIWDC6z4Ytu8Hqj5g5AZop4JCY+WYrEwzQFg4KAKbPjcWtlGJfjFKbxPLtYVydrGClF5BjRWx9FQqb0buAAbf3G6HBFWtzY/XsovAUEjm50A6WKsvG8ATvB4PmGBWyGb+9D5G9s+A+sQxqfbkUQ3GyI3vmsqRFEA9iUQ9PTtCBox/r0ljCZnrDc/Qz/4r/7f+Dd61/hP//5/4DrN3+O86yI2JD0HR5yQ339n/D3/xk4/+QRD7/33+Hu1e/hWpjleQHx+HzEOoKpacXgSMEajs8vjyGPorkIWUzS1Rgq3aJ9CxSU0fCxx8DF3ERIp3aR+pExiQdjYYiljQL2QabiOLM3BLLUJFCXihg8nUM4NB+yXkDIq6MPKHBdtxvmESmR2RrXrBYQiPlT9DLs16o+cZC9By5jQVJIHVFugKLb+t2zjDgcosPYfhypm1wzrGmJUgajtkaVhFqMgk6D37qP5zVpfJ+LonowsN3qcEfJDwyjf5SbYL2HtYpg7+6YYSyz9U9ZBhViMjbb3+F0Og0Dz6a0256wEDkT3jMqp876/e9qDbcKFN93/IiaQjj86cXAPSo8fiGbsoJFSe9nE0fDoapIId4Y8+EshhPwDeJZhysa7t5vyMLaCx1T3iw1hhj+mCKg5FzrsqCWgrxdkaYTpo3zlq/rFdfLBbVx/kLvlCSelhmxch4sC15eYOqWVmdA1OY1xEPkYEwawdhgMUYWrFo1SIjjC2GFJUnefciCt4pj/IS8ECJyXkgVNFYEDfUOK/gzG+/F2V2WpLa2U+3Y8e0EALHi1G4IRhTSI1Q6Sm/Mxrp/F6UwvOjaekevNhjpQIcjZdmurfcR6Xjx3a+TnaMFZXOdpUrFW3VFyTawd56Xyzliwv2JzKaqDZd1xbKcob3jl3/393j97Wt88dlLLOkBl3aFnn6KOT7gF//NhP/4P/wt3pWvcTdFpP4Ooldcv1F8+6vfYPurv8cvasY/+2+/xDK/xGXb4GSIvfeAmUMrJnE+uoZpEATBGCIH58u3NbJuNQfrxicOA7G/v+P0Q4YJTq10aDJaodsjZBZtg0WOR2PJHhtzZnQto9ichbUeQCz4cSmFihAiqg12clZOTvONwdqfze7EYsiW9TJjTMnhWJicBr+rVhvlaQX9EIRZshBK3aFkEi48GNufq6AbBNtas8I873ddr7heL5zO1jvQO64bZxh8yKkcWVPMnjuOas/jufMEw1Esy2LaR43P31ARp3iwIY1KDQ8PL0evR1dBjAmn892o9ew6Rt7AF83GuWosn/GyLKbGSpqtd58vy2IjA3738aO0j45/HqGe4+J2T6mqIzLcDVQHNOC5wTo++OP3Pc8qDn9hqhjCYPnANiapllZ30Aj4cI+eoEbHA8gUgnBMKD3zhpwnbNuFUt0poW4byrbhciWVdcoJMU8IPZCjzFseEUoz1gyjNmJsQbzArGNzAxSrQkrWGWl6Ln1/xnrMEDoOaqoddStA6hySrsanrrfpoT8vj0hG276y6ATLVErheNMUI6qlx0w546Fxbn8nNF6Eohz+slc/VHHJ9oCxlRqaSQ3EEA80ZH9mdC4qQDPan6+pMaDcU19Vjuo8UAd9Wfitl7JRCsBE0aYgQCuo24a3b77B0+MF+Sc/QUfDVgvuT5/i7uUXePnqJX79V/8Tfv2X/28Tjas4pYBtew3pAeXyDS6P36Ktj6jthGp9Ld6hPd6bSTWgkgXngVNriinPI8ofRX4wqlfdjdAu9bzvNXcSgEN5uAmijkbSf8e58T6JjA1SutNHO/8tHmQoRme5ETmC+Iz1Wzopr9+jULJbpmm6oZ+GgNEY6giDO4gYg8GZsARUrXeJQV+QvYCeYkKOLOz7EYKxiKA2ea2PCN/rK7X2Q5NZRe8VtRRcrxes65Xf3f2eqgVuYdikVn0qGtdbs9GcHlTtWZHruGH8ewiBcHWh4+zP7KazzlrruFwuWJYTSuF3zcsJva82w4KZ0ul0ZqYgh4DgcM7hdGPE3R2b/LJRVT1j8PXzu44fRUk9YpHu0Y8X6OkxYXiFl4IdXrG3/t7RDas8Rhn+Yp7f9E0NSVhLwIjEhRmKTX1zkT0RhUSBtmDsgY48zRwZGTltTEJCiMk8shUpc8EaLti2K1TJpokC5BwR4v6wmRHxmiRExLhDJMHSulIKtOxZDIt4GFmWF3o9CuxKWqd4LBhtghnMqFdgvV6J/wLGZDlosh8iQs9M9qxLxpQztfqBS3SM7MxS3Tierxsbi87A33OIa59jy/fpTBYv7sGxXdvAe0dmA0Ig5bF3PD4+jp6QYvguSts3Xt8Hm7tT8HcP8GfX9WLQDplV3e751cMdXt6fGVf3TikJRFSNgC548cUf4ld/859x6Q1RZqTeEfKCdhXkacHLFy+Bzih33SoL9nJbwGs245fNgqQyd1VM85lzqoWwU9R0gPr0oM8vB+PMLOPYNcusY7vp+fDfJckhGbwrnOYn5PJzrXqWxYygVUJaOe+KAeP74TUKzjXZto3QqhVgl2WGD+bZ15qO6JRFUcqrALDpZl6TimzcCw2Av59gQRQVCsK01wxVhVk0wpjzrQYVl9XVUpkpOFmhtU5BvF5RC5VXKbRYsW3rMJBU+m3mWAHKf/isDe8mtgwewsDSIKwbaJxLbN9jtq9iCDb8itd/JMwAhAUdfvPi+5u3v0HOGa9evcLpdAKh3XRYG7foixNVPHDwjmZ/P76vR9H+dxz/oEKzMy92hwBYXDgMkhehbrxY2l/0DcalOhgOx8zjWDi5fRC3dYp9wpeOjSVegGFLLFQ53lMDXwZaNSiCAnghT+iVA15CzMjThLJtCGFCnia0uuH6dMFWK2onYyhZ1OzRDYuHMNaLL1a7R5ERoQEYxa4UZ0xTxrqulmba/AaQV+7PsQ8MXyyqA7btConW0CWJNLna3ntuqiDm6t4Lgtp3UT1R8vi9huA01hgjeuyIUxg0R/GaAgAY/5tSAma0YI1A/l5iAjplMFQEmjyKbQaZVZzu7ugEtg2wnw0j2BqkGZYrBCX1MOBkOCMRKBpaLzS6ISAFoG5XxrwWAU4po1wvY1NuoFwEKvDpT/8En3z1d3j9d/8WU4yAXpHjHXS+w/1nP8fDqy9Re0CeJywhQaUZy8lhmQYRNitqY+Pi7sAm9L4X4rvtm/Ge+m5Yib97dOfaX7D7LYAGLMsy5ifU1lC1Wz2KWZcCgL2zEOiktkL5lxR8LrFj8ofeBImmjmr6PcoIXoXzBqaZshjDuAEjCobA2HokSrgkhXbPhOxeRIbT0REoOgR3EKMbBr7ZfIDOYuwgKhAeipH03NZdamLDtlUQGerMQC2DFAtm1JvSmo1r1TAK5t5s6ZH6noF5EEJblmPaG97gdUUxWLihmX2EqQNoUPg8Zq+t5EwJ+9oapnnB/d09zncNOU9484bd43d3d3h8fDRG3i5bQeIN6azOlnK76kV4t4/HKYi/6/gR8JFHY89P/D7EMyr+zyioz9Oe/QzHSFRuzvPcKXj06ROWjrUHP45GEeO3DOaxm1F+cIjSiaWsDcA8w6iaE0KgZEZvG6bMSWdlW7GVzfSc1JrE6Mlr4fzmELyZzqUy0jPMEyPDIZ5qmu/+nEYCv1MC4c8DzjahMd/aZun2rcM91mNuny+zqWMBH9gj0uO/KRQox3rAgToJh7V0ONhjWs3v6nDoX0SA3sazAYC7uzs8vHyJx8sTruuKaaKwoMihS94yDcez/X3CI0kQpmtV0KVx3GYQ5CDQRiG+2jcAgmDnGZ2sXvNBQsUDvvi9f43r5Ql//zf/Bp+9usfL86f49MXP8enP/1vE5RW2oqj9CXFZEEIGt7uv0chipdJxQZtJdAu27Ylijpk6QBIY0dfKxq8jxfGY5nPs6d5R75o2o9HvsCZUFc2ET1zLyAhpNGS9ofeEFMOoT03TNAyvv093TL5GKXOxN4eOzB9tQEjmY63mEfd9DWa963qBQ1RIsn9mFJwP0BecfrrXC0PouK5XrIUZLYuvphTcqmVwxTqjN6PZGoRmAVUrm0XMEdRYc4ZWGA7E720nsfhS07EHeD0yIN2bLmjdP7t3Qfscd3DuNBj5p5RJ701pZGq1Vrx4+cCeqRTx9u1bXLcr52SsBlm2hCHhb872iOJ4QKeHffc8sP6+4x88ZMcNvBfZbr74mdEfD1Xx3oWJQz8H53J8Af4947MsJsDgx5ub9qLujRrk4euO7IQkt7UMVUUXFvFymlDrhpoLUkyodYK2gjIx2tjWlcWc7cK0tDZsalLZQpkMj3z83M659vtnp2QzJ+DX7ROqSE30ZiVnkgjEaHEVW6kwcrw5g0OR+uB8j89vdxh8eKoHff3xbBS97+9JodT4MecnCiCapIhEG350UH0E9iHpIuYsDu/2WQaZc8bT0yMen57QO/njjpFHe2aTi7qhA7XdGJDx/oJaY55aP4mgdAXnz5N2KyJo0iy6tCl0qkOnXtsd8vJT/N4//+9RSsPbyze4vJ3xi5/9PsLdT/FuC5CJ1yGtD2ZMiwE5xSGBIdg7yJ1AAQnYtidmdxKR04wYM1pTg+5oOHnOvbDIQqKO9zTPcRSqW2v7szSnUJs7kXygLEYEobBjd9pj7ZbZiJ3LDSKhnujd88/2kO/3IxtpN568phSYMa/bilra6IOIMRvf3tbBsQZpBXP7yw0Myu+iXtpQA1C1fgIGZ6qdysfedWxaXb4uvbvahQE5uIqyLxCx53FbxzxqNR2DqgGRH4IvSoL08T6ONSM6PmsCjIIQktGuT5imGXmaDnR7xePTE2JKuLs/o1ROb1NYAbuyFyh2knN8mt4NnNj7QHOO9vF5MP5dxz9S++jDnofpuoG9Nz/QMTTneL5jp9/xwj9EoWKyIhChbsnIAp45kmdf++x/WGrrRaXDwmYdgLWKlCbkmBmBtIqpVsMoN2zrhm19xLZex+xVRn0BIj4mkL0TtZR9oEgM1pYPMhhuHJsbKoEEw91bQ+3VIDZYhGUifp3zeBECajWDb9IW7KjVmwVxbEiDGCMlEPa4hfUcErRO0RDRRYmLHsb/TRNZItoxGEbudLym0Loa5EOoR2qDgtPtRASXywXNCt4QQiEjUrM6wt6Q1xE6u8r9mR0dmb+/kUKniHmasW5XoPPZb7XAi64sPpIqHCRCZQLSA+b7n+GP//T/hl/98r9i64rTp79AkTugJ9TLijgFbK0ZQYGwxhYC8hSRYhjYMqm2ZqREgO6DXiK2rSJIAjvsE+Z5QUoZ18sFvXfM8zyw4W0jxbD3bgOcNrhmF5+zQYtqchu+n3xtDWhXWYOq1aYS0oAwYs0QeEYY92xOZJQCqfvF76oGg+jBgBMqAULYsG0Fj4+PI3CEciRqMr0h2StOUC+C3+zZHYo+np9Gmg55264MrgxGKtvKdSsOzZnBrpRA2baNne4Qc9od2fognrMcj3tljDodGdUtpdrXYa0F3hvi76s378IPyDnhlFizTNOMeVqQp/09+zAiqvt0XK+EG+d5ASDwwVQkDjiG4E46WlNbGmvnec2j6T9xodm1hdBhBVFvjDBjLGwi0hEZYTywcTyL+veXAHsSgFNY+KLiwe8wo/D0jPBCHC9ofJdnHdZsJXYtDiB1LwCbHIcCCKrQEKBJTFKjGrM1QJRCXNmKor1VtFwwTZXNb6crpssFl+sVtVoxq1aboQDD3VkEbID1MADJIybbxCJWXFawsxmKshbQZRG33Vamvy9evERKCevlajiiQqKpVTRy0mkgKH3MyE1GxCnBi8FAU4yIeTzXwYt3OmWFSBq1jmbvfKuk2zH459CZ4zsTEGbSAX/thU7HYQeEZf8u2CUwVNWalcBhNDGg9AI0tQ2fTDCR39N7I23VJL8FgtpXw+EJcYkA6B3SbD10Nk2xOFwQAlD7BCxf4JOvXkBSBuYXaOEM7WLMomKF+IZqaz7GgFK4QVM2MUUJCGlBU04DjDkCGgbcsLWGGBrOp8+xzGeEELHMD1i3C6aJAUXXAAlp4Petd1y2FcsiyCEZmUOsg7jb2vfYmw6rK0wluNDg50DJcgClFigE0eYdMMNlNh59IpnstSKnpR6boYIFKlCFakSrgm1tKFsxSefEtYKKLhOqEnj1OSMO6fqh6leOoURK6iyZPKWSqlxbIdvLisG1MniKiVAsjTQhxPV6QW8NQXjPtWzwuJVKo5SLoKKq3Cg973AZbO0WpCi0Ba6FVPmOj1D2mMgYImLm8Jtpnpkl5kQ2WspGU48WZHh7nmDKAadldzClloGceg1oyhkpBWquJe7RYBI6nDGhw7n/QPToRxSaB01NHYcB7LHRQIMv2MTr/CM3EEbHKMj4vzEasE5FM/oe3faDwTyum+fZyjG184sSX9zYsVIAo+P56OmHRw2ASOfM3tgRO9CjQTiOSzZihL03pJzQ2oI8nTDNV5RyxVZWbNcrtBfU1qDFuh5TcCIzFDrmHIsKtXMO9ZcSApIoRIGUZ0YntWE5JfRW8fT0uIv4ReG5u8mT6yFKMe0b4sACp+oJxOohMHGwvV0/BoYqhGjoPFtltB4kkF5qhgYmFhYkQHtE6h2tc1GG0WRj+k2g82mqKJUdoYzwLHIDRhNW7cSMe1fIJLher5hsJCHRQ6d5NpNIYTOZZxXuYHovkIKR0nfrQt63+y5K1rWMrm1JE1qfENIDEBJ6yADyaB6EdpMxJ0khJW7QUpja185MMMaA02lBiAtCJSzWLEOCMjY+ne4xzyekxG5ijQ01NFNQZVUpZ0W14vq6riitoq8XpMqBRNQocqceECMdJPesz0/Yaa97R7LVlzxuFxsoZQehJ98n1g+him0rIzqlYXWMPSEhsQm0NvQOg8YqupBlJJENdV5bwbN9KLJLRXsPDe1ERd0uKHW7wfA9g/CovHf2EHTtuFyvSF6Q7YTLUuacilbFCtIrAGCel/FMcs5ImUa6tz5qA8zOAlSjOSiysYr1L6ivK4PQRYT09mlBzhMdgsm3xJiQ88KRqpmyJ9FkbqLsvUyjcc+p4MMR77XBvabAQnzbKoI4zBjgw5SOYvjfd/yIjmZv3wYwJJ3dGBNm4MXCFtq+GQeEccAR+Xt2k6roshdLxqLUHY8/FkqO7IQPFVBuCtfBY03guRN4Dk+peaUYrGtTTdenNpReyLqQAOkKjh+YoZqR5xn1dEItK7btgm29YluvVGS9Wpd0bwgpHmSSA+dEhEDD6GkQAGjHVolHn9OENE3oqaGVijwtoyOztYrL9QqpzeYnm6a8xOHIqnLObm9qsEBhB7kccEi7/4ENH94b3xPMiLn4ISEnL6x17dAA1NYhXRAa31eOmaMPIaRqCrnxrVVosCyzsXYw1ot1y7ZG6XMvfp/PZ0A7LtsTHCt2gxA9O9Sd+aZqUJPh0HLosKehdOjRaJsdzCCErBiIyXV785iwBuPQELtIOW/Z1xF1cyq0MvqcMievEee34m0MFg0GABEvX7zacXZVrOuG9XqlXLRRDaGAtoJarxCRAf3wnSk0ACGkw34gvFjM2W/bhnmacXdesJoR9Iwgpcnep1NRd40rXpO9GHD/1kro5jiOUy0YIXtHMS8LWi+4rhes68ogIUVAmT20ximGKe7yGcd96XUB1yDqvZN2W1bUdjun2IuzRyfB32m4Xi4DTvHzH7XHvAmWz/0KOoSJTV4COApxrFkOpk9dmbUcHBfAICvHaM5gwrIsbDK1noGYfX51tIbCYPBUHk7BAyqHrW734p69eBDADKeyDmoOymsUtJ0JDpX/kOMHO4Xvo4fuReedR0vcemcGeQoTRN67Mbb2h5tzHh3BMSV77gC+6xgv8wd89sa5yUTP60bLtWei4X2eVHQhg6ErkmbkaUbdMtKUMS1n1I1O4XK5YL1cUFvlf4VT2hxf3e/TIiZrrguRvP3LalF8FCwz9fineYKIom8FSdn0tW0rXGsmpYmFxhwBp9R5Ma5WaKX0dGtKKQ/dnXUIgCR3UMwWnCIZghrKx897d7R6ViIYbCtVoOVmTW4RHUBMmY5VWcBsauyvzkhfhWyroAHrtuLl/UuodpzP92jNmF5e1OukuHZV9FapOqltZDkQ2PhLhRi/n2uISry1W2OhHIqayuiaD6GPDGcUOolzjCysa2PBr9PpiwSk4JIEy5gUVgqb91Q7YsjImZCkR4fa9xrXPHEOdm/gNDxjEc1R0GNjJGmsk24y5SQx5HF97jTpbAJgoyljzBBPnXDQTGJrBSnDwU4xoBNADKblKEreA3sN8sD4PesQAS7XJzw9PTFajhFNWdTVbo6jdbTQUILL2zjhg3uLUFHHuh0ntfH9iNw6AgDjM8e93wbs1G7+N1QP0bfVklQpughgShGTzVbX1thPYXW02hrWlYXf5oO0rLZCJhRx/2li9/Bkqr3xIGsd7D+SCDyA3RsaQ+Aaer8f4X3SyDFLorzFyjVmDmGn0xaOaP2nntF8pCoeL/Z27ush2sQOH+0R6O4gjhX9I8zk53r+3/HhHD9zPNfR2fifKntj3PFBH0dDjusXRjSjO9TohDlEjvLzphYRBLV5CU09qKB3b5m86DxhOTWc7irW6xWtFFwuT7iu15HOb1tDzt0E9vxZqSl/KiQwqli3d6hrQQqRE816M3xUcDqfsK4r4gHPFDN2MQrmOCFnRW+K1mFFbzqHlMzZ6i7QBQCtyeGdZCtGe2CAUTPovSMIGS9RXNOFEggCYq4FIF3XdHhgg3SoLsqxm4dp3sTiAXz5xZe4v7/H119/ja4Na23ImXo8ZXSWHoqDvUEM1hKDOEmvNImJ4PO3PbrtQAjQwToZ6CXrUHYe6Z1QiIm9qZAtVGpFiCtSnAChBAOhAaOcIqI1fjc0AGrsscDiskhCihyk4z8j4wyY5tka0hSlXBECR1qelzt0dKzbim1d96BLjjOQzVD2DdfL1Z6NMtsVwn0xZaSYx3tUe5dHITqxTBLitNK98OujOX3fcIaHrR0rZgYLokJn5lVKRasrHWfKJuCoBst6vcIcg+3pVqsNEvJCOjOuWpvRlulMj3LrvqVd/8tng7sjcJg054xostu9EXL0ekIMHgBwXTmZxJ+RS3v4/U/GHooxDmeQcx4ssaPdVLdLLnEejpTm2/4CryENG2X/Bt0VhT3I3okzt1kUnw2n0YUQgQf8zuMfzD66Neo7HHM01Mff8X870kA9Nep6yxo6Gnw/np/v+Nm9Ye4DEJFBEjeVeHyY2QRxbJvptAgQEgXLolKhdAzfDjR2VIQFoQkB5TSyotY8IoycT2itYJpPuCvFhvw8odbVcOIN85ytkQhQLQjKwnMt7KKWGLGuV7QaORwokt2yXTdbkGQeeeTj9Q81KEkCEEUgIWMOCa0LrteV6WZznZ1b/SF/xzdcdlWDeUx/SPhMJChiokZ8a/yuag4upT6cgpoGT+s0ypzyxsUNo2Ii0PCoKqaU0Wwi17qtWOvKLIBKb2PTdahJi3snO7/L4RBG1jqMhb1uFqot3bYyLcRqX4IOrR1dSf+lweqmTivQWrBJMfkSvo9pWpAiYTyfata6NcjBexkIM2jc12nXzhnQvZvCqtVzWjeVzM16BTrKuqJsG5+V4eVwOXK/ua5oh3GSYrDGMi1GLkg7nGYyLMdAaY+kbV9hZ+SkmNgIenAUDg8ze2noAEJKSAJI58/LVo0YYc68e5Z5K3Mz/qRXpTHsDbXdzkU+BkBeAH8e6Pm5pmlCTgFPT08IgaJ/2pgBbW1DjByl2mpFsT4ZEcHT0xPWtYyZELDM08+frYDse8epxMcg9OgYuAqM1SdOQbfA1teHLc73bBqO6Ir/x73GtZFRq8OnOvZxrT9c4gL4MTWFsbcO0gIAGQ4ipAnajF2IsZPSLr0QAvGzY6TvRiclMl2Ox3MI6bvqB8cFdfzskUf9/DvHPR3OG6xY2U0GWcLBEQWBpIjQFCFktE7MEt1Safve0zRhvV7Ra8OcM6CK1hV5rmi1YsonaKcy43m94Lo+YltZg1iLNT3BC++KnEwptAlUAjfGugEtcE51p8ZQK8B8OtEYihWe7L63WqAdWJYTAEFQEO8Esw46Lls0DJOt94BNVSmRzVNN7z1mvsdWG0pZLQsTNgI67VsVte9S4q0RmmuqgMQR7Uvk0J8IYvaInButgM2cTewqdyij6ZD//uTlKzw+XnC9Xkedh+trZ8hQiZTQnDsOQMdM38HGEidtiu88M7DBotwChGMB1pxQAELwvgh2Al+bWvPjBO2F0bd2NK3sWp0apmmGoKLUZtz9aRhKBdCrj7aEwUKdcyHArGxdWRcoZUNGRm3AJnQSXkM44uzA3lfQesM0n57tt2OT6P5vxZq9js1cIYShxnoMHJyR1HoxptM2HFprddSpHIe32Wom276rEhy/x2mgLFYXqDY0w80hZB/2ppYlUqDSoWhmJDOWecE8TZinGRImnM/nAeHC1mdKCdo7UrqFo3rvWK+U+Li7u8Pd3R3++q//FiFGnM/3ePnyJWotWNeN4z5jxDwTuk2JvQjOPmLQxUlyFHkknTuHZP1KESnSMec82Xu7pbd+KEhm4EB4mdpTVuOADQOqu1jnD60r/KhMQXeewo1xvpHNPuALR3EztYjMb+hokHt3/PrDUNHzgvNzqMk/c4wUxiIfTuz96sJzZyPY5RA8iuK/dmgPKN3zCLHiqDyLBuJIzbkBGidu5YnqiKlAe8NUK6Z5wbQtKMWUWZ+eUNuGboYkqiIo2TbJsHtEQUUHeiU1UhVgrQ5PT21MU/NrCSFZFha5MeFppnrwbEY9YZrynm7ajABPpjjY3Os6im1bsW7riPBFmJGl6Bi51wq4avhF1oFrLJRqKbs0QbM+hAbFVmgcYmDRb1tXnKaZmGzIqNcVMVFLPoZgxUplSl05i9glqLtdVzg6eACwLPC4Vlzdfqce8h5SZsF517jywpLaoKM+5JyVf+DSHlFKxTyd2KfSXNTsZMNR9uh7Xa9wNdT9uzH2GJlNwSa9dfRWjGXDzG7rHQBx7W2LNx3RvmdoEEznR4GUl11jC3svxaCbWn1EBDdKtG7YQ93Hpg7Ir1NWfjM5ibJtw0F7cMZgQ+HYjENmjJapteUUU1QdTqxZE+B6ufB5ew0IwDRFyw73Z+bvdJkXK/hzP9a2mSCjQMBa0PV6pbwKZPQPrRsdW2+HDCpGnO/u8elnnyFPE+4f7vHqk0+wXld8+/pbNqNN84CqaBeDvWoZ9kBBOnqrR9HDvQv52C9xPI5F8hsbaU16zDK419eVWm1k+Snu7+5H5v1Djn/AjOb9eG5o9787dc+R4uMN3Bpj/7xvlA/VEX7X9x//fH7jvvmfO5vnvwu7Ak+D+bOAJDu8QcYPIxbnVx+vl1jltC9QS3MFAo1AipkbOjekeULaZpS6opYV83JG2a5YtyvWyxVoG2rdoL3ZbAVvHprRGmmiFtsC6oN99vtjpFgJFcSMDduQbWbR14aahDAKxbBoSyKjVNJzNwDubCJqBUo1DfoYIHHXtoF1VTtfm1LXapEii8sdnI3bekfQDrSAEBQIHiVS8vf1t9xozSCE1ioExJkFguvjE1qpbHxsDh1YkU0EXbn5e+9IIeHly5eIMeLNmzcsiuM26PCAglCbNRB2TkuLOTEcUqs1DD0tyz1szaiw9NR6QzHoaJo4XlNjsAJzHFlXa6SettbQCzHfaZqwLBN8cA8DLq4n1WpQSketBdBmcusRIbLGoFZLidGL4yRE9B6MBjmZiCEhK0a6zDxSitgKM9kj/HDsLPbMWxWHaDbcOIXeXIPrWKdqaLWM/MQDLt+toR3mkdiQHs9IRuEYx+vYIS7PKNwosmGtYVvXMbPkWPushVIYgGJdV2jj+/KpgM54SimzpjgvUAjWUvHy1Sf47PPP8HS5YNsK8jTh7u5+OEiHfgAMEsBROSBPFBJkYZjON6Y82FzHmutxbR6H9Pi9A6RxJ2+GVSDnhHUN2G7ovHvN5occ/yin8Dzi3//dIsecRuWfg+p1iGftUT9so333+YFnxvtgiJ/jbh/KIPxXnzuP51kFwMiPuY57X3rwecq4P5+wlSvW9coHbrovUBY9owAh29xd4TCVYy2lh4gQnbXC6VuxJtQ04XR+QK0ro/DzBVpWlOsj4ShX3ewNyzwj9Ej11UPfBJviOLHJhfla10MhVazjlo1E3eGhyEg0pmhjHvdsac8cFLV09LVTt7/vQ3FiYLrsAkekEm4IwWFBjHMgsCej926Gmwau9T6gxa78GcXIAHRG09t6JawQyRS5Xq6AEtM9atuLXX/XRupu5BS06AKEKVAszYwnX7s5NYmEDw8SCaU2xN5YUIagunZ9sBGZALw3RMc4UwAQXLSh1gyJEUlnyNPTzTquNqvAC5RBG0IjvIdg2kXmVGstKIW6W7VuKNuKHqzhURKkecZKR92N1mj9kFRMRYfmiFpWUBeILJ7aNl5PC9i2Ddt2vdmH3peQUrrJRI7FZtdu6nD5eB34P9/LTg8eWUank+U9UkTxiJcz6CH7B4NpUw8ZlBjMSTvCZjEx+CajNx1FYIDrYl1XvH37Fuu6IqcwaK9baTbHgP0JeSKV1YvGPI+J/Rk7iFAPZxW4VMpRlM7tj8Nvp9MJy+kOl8sVQMA00Ukko8GyEL7D4XvWf+sobgNZR2Ka7YcJp9OJBDoxAkEveLo83vSgfN/xD57RfLzA74vq/YGoGYV4oJbuhvv9ArH/7xv46VA7eH4d3+kUnp3zuSO4qVW4h4JTZ8NgFokE5EwDmmOyQvE2IgSPoFwuWCTQKOtuYDRbxGPpf4gJkthdit4Ra0LMMxASqkWN07IZPruhlY0jHa3PgSNHA3IQhHI1xgoF0RhZAj7q0DHbNgTllI1A1QXP0sF5OqzHaNijFKpKNtYGsG/wY4TjbJFWG8q2oYdkToEFx2bRYYqRNF9EG8JOqqSOCMGKkK0R/vGmyKZo4qJ6fGcuUQx09FqQcgC6FRBzRm0Fr99869bR7hHWd+LMKmrXeCd3N5pljBHQXdV14PQCILhhtDDC1slYW62iQjmYSGESFeFms8/zMmoAImJ4PAkCOXjRv0GVtONtI9xRthUbKNMAoU/ea3Q0Wq735HTJ61UR1xU5uzTEDu/07rIVDECORAw2eCl6n8wp1H1N23e6gwlpj9y9T4KDcXwWB0YfgVMvee5973kGAmBkC2RC7f1RDtNw/XFtjyE4BnN7kX1dV9RS0DszBCoSV6D74CDlPINKJuDLly/x4sVLnM93iDHx88aqSymP7MSVardtGxLX+mxvuON08TvOncjjPBIow0+RPr9nW0dhJ35IaJBAyFjdaPIn2HvGuG9yzhDrtXLHXEqhOOMPOH60U7iNwA8R+wH+gWKwQYIcBsDj9hzjvNatJ7JjvcyYnbt7y2h6Xmt4XpS+1Uq5rXP44jvKf+8Xw/8zOnHtrljo4+/llLAsCa1693YbxTJhvoAUIxQcwQjsaa4KoYkmAulk3oQYieG3hpRowB7fXbBtHedlwen+jKkWKBrWywWtbSj1OnDbWhupp9OCbkUmMQ6+OwZ/rsWiGWsWIDQR042R8hyYDiWMxq9sz7bZJnKYRSQBGgBTCFWwB2LbVvR1ozYQcTZI4Lmc/SExIYBUz3ItZlsDggbEBDiBXgDEFBEl4Hq9AmiQREx4NSzcoSqVjrt0QswJ5XpFW6lWqpXF7N6taUoAk7GBc9BDaGMovaoiTxnn8x3KxuJuVx1YfKmbFaJN8E+sfhUSRD3DpMFrtRpBoaKHvTFpyhO6NR25gN31csHliaJ50cfUQq02ImP+dmsNdbtC5g5IxGbDXBi8TAgpjUFQ3iwFFcSmeHqqpp5q+6QfpZ/B+ozBihAZcJLvLToFHIye0R97h/RkcwwoYQ1bJ773yrZZVN2gebZC+m0UvMNlOjL1GBNqiwbfKlQFpXAeg+rOZBzXpB3rdRuOoVlwMUZVgg10eUq4n8+ABJxCxMPDA169eoWYEunmQXA6nUYWkGJmUdl0hmIMWE4neHdxbRyWRcfYWegNAcu8IMaMrh3zcrYghow1r7AAO+nF7Yl2dwLAcQKfP3cO8+p7jajToZAAsw3T1m2f/JDjH0RJvXEOQ7rC4ZZIHR6GTQdt99ueAl9U9qMhwTzSRqPDCboHdztfQm7pXv6gPCK+Uf1UtWLr7kndWHoXJhcS7y0IzFVTXhmHjIIibgEREfPpDjHPbF7phwKQQQcCIOc9hXSskU7PnKpaAaxSxwWqmHRG/HJCr5+btkox/LqhnFf0VnBdn1C2K0otLJTVDZfGDRjnBQJBLYXDyHvHPHGgj0Hh7B6HoDcO20EQtAq0wwZjp2bBVtYR3Zjbx5QnW6D2vh0KElJqYxYki667sHNU4eJ4wJwnsPGPsFIUQYpM7SUGiEaIEosXBdRgra1ViNXXSyUO7gPbPQOKKeKyFZOHbogK9FWxgRz5DkGB2sQ+3s/9wwO+/MknuF4v+O1vf0MhxEwu/TzPSDHj8fJEzacQ8dMvf4Kvf/Mr3J1OePPmHUotcIkIgdUbOkyXyIxppfQAXC6lK57q42CKzFODepG+c620utMYU6KxLaWibRvatmG7rNBK5slWNpRK/PwqhBe1C4uaeeKM8pSg6jMEvDeh4aiWGtO+N0edxYxQFQYKdWP2mnO2KXeEbuZpAnpFqSt7SSqZYp0DGQYiMOWJDlxZE0spHfj4skfZUKqIpszsSQXaOVRILUNLpvfjzqP3isent2hrBVrco+rWUQsNtMSIafZgCIzgc0K2DuTaG3pl9jDleez/YH1DCkDCDB/5O6YJCij10vog37DxM43RrN6B4CjCjpLsEuSOpgyYbUDuu0Pwd9N7p9hiEMvwhDTuBlDZYEMrZFAde8i+7/hRmcJzQywQPO+S8xtFoPEFdIiZKsBNgcM/COgwjgXpg/P4EPTjnzn+2xHDew4xHT9//N/HCIV/Z6ruC+xDn/dIg+3zEb1n9L5nLEcv7i/cL2VXkD48F7uHmMLAWZcQoC2z+cuLc6LIdUbvFWmex8DxPF8oEGbTxqCdozmhUGmYFwqfNevODJChzppyHnMYqOzq82aJB7Mz9RHzPGHbkmHfM+dOtM0iUBk8fTpbUj1j4vAQvoe9RuF690Oa2QzU0JfRiDynUScgM0cMftkgzYqcajABI4ox/Kd3RauK3gTSWVdQoXGMIaAX4t69MavJOWOeEuYp4/W3X4NMs4payJb57a9/RQP+9MRV2zrevn6Ny+WCuq1Yryv7V1QRCgvqMZKFhtZQLeucsgDSKEUd9rUpoDJs865sg3xCymRZ2bS9y2VDbyYPvTFTbGU1Oa2AKAFbL9jWQpG1zpnQEoLBDoTtglLTnwalWU2qG5wmaM/UZv0deLAWYxxMJac/ilA0cd2o1Arft6pQbWi28HPy9UY5cM4Bp7pn7NEyTxpq31NU/G3MzMRrTQzivK7QDeL0rt7WGtAEYv0gvsfSNI0If5r3jDDGiGmeTcHZ6jvWDMYM430snnWkfe41hoHHwCZZ9Oa1etHZUQsPDN8vKiscNfE6DTPLMLKeIxx82xvmdTLBNM2211Yc1Wx/yPGjnMKHCh3PDbB3ZY6c58gqwg6lfOh3nx8fqh98V3H5uQPZs5D3i9If+o691vB+BnL8jtsX5SPvMD7rnY5HZ+B/79DD/et4RlEjFJGd0K0ixdn6GcQ2ioyIpmvHVE+MNluhZn0taEYF3LYVZb1iSyta28iGqQUK6vikGIEDvZLQ3j6xjgvPaamMPC6XC1UYcx7DiIC985kwny9qsoR8yMcxEvVO0tY6SrnamuDc5RDIpd4lURhF1kpq493dHZZlwvXxkRCWOS8xAT4oi79arW7SYVx4rrkGkO5YCg24SZiXreM3v/olvv7tr4ndQ9FDRTRpjqendzYPwCCHbcPjuzfotdJgdTbhscZTbENOmISRozZSfqHGDjo4Bc9eh0xDZxNajBHTciKzJNJJUGLCis3rE2ohC26e8sgsfH5y9IE8stNcOeu6QoRrwTFt8uXL4NE77q9e2vG1q4paDkZMvTbgjBghUyuyadPnL3u9KsZoUiTdDLprDx3O2WFzMQ71ii7IU8ayzKhbsZoG1XlLJTTnVPlSq60XIIVMMTxhHTPlbIQDykx4AZr71I07mx2107H1phbccp+KMAsAYPBbHU7sCF2pqjWx39q/50b56BSOQaWzuXYZi2aOcnfO7shvlRoYWjNTyZjnE7atYLuuVlD/J5bOPhrcIzXKI+ujNtGHRjHf1B8O1vJ5Yfj557/vWr7LOe2e830n8l3XMB6q3l6Tn8vv0V/c8/F3z2sZH7x2ve21cLiMGQPHhVrCSww9Z4v6if+y1V8R8zxqHbls0N6xrRc0Z6gUpoy1rFaUvGJbL1AAa2EhOMUM7WKzn3kuHgG1bQaLbUhJoBIxzfsQcI4jTWO4CXob2B6xV+9PCaMI5nUlIGBZGKVt2wZtFSH6O6NE9LquSC2YEwIu10eUuiLFiM3YWBjvJHLfqosB0hgHBbocZ2SvEOPJc3yo9TN0NwoBquwn0NjR0Ax1sLS+g7pRRlvMKWFdyxg2n/NkXa9xj6bHEgjQ2DCGDalj7G5IGK33aoq6nUX4pg3FMg8fHXlU6e2dRnCaZnh0GaJHqDYlD3tjmY46UESxfTtuTkm/vcHm9TDBsJNrxaIxIAjgZLkAL/ayEZIzP/y542D0m80wKOV62CdhMHtSnkyKRBCRTBOKlOfeKq6XRxaN7T16dunXnFJCzMT3yaxjE9mxyzimROn/Q7McQPiTjtIl4732tcAbC2+6km10p4jTZGU4CN/X3fp0jgHr0Y4eWUXH/5wRtduKZoXuYHuwW0bPjnfPmkIIhgp0qFIGns82Q/X6wYznQ8c/aPLaOD5olMncUbk10t8H5zz/tw8Z1N+VMTwXkPpQRvH8fM+/z+Ge2xb69x3I+1jfXkz/zhRNyKGGwRxhpHOMigMEWYj9jmcpgKY+dGe8cGirjV2YyqElUz4B2mzwCGfxrtcL6rSi1zMdRa/Eg7frgIkkcPMnowzHFIGYid2vzPoY3LLo2fpzyh0N1Zg6pzAqpTuIZu3/E1QxsM3BdgphRIs7UyOg1g0hAPNM3PpyeWTEP7julnV1boQIAYfY2DuzDVe0o/cwApkQ2CdALJqNb71VaBPEaHBHE4s9BYpA2CpSegJaTRa6odU+Rq/e3Z2wbtUaqcSGtTN2661jmtIQE3Toy58RQB2obu+nRU5xC6ZGfOTvuyQHC6kUXAwhIUSXcpiG6BoH2ghqOzxfpaT4iJDD8/Ws7633Y2Y/1AlCQFDCNw6pAtR6ypHNkABs5gEbqUrZUFvF9fpkUtQZy3ziPIMkyIlwEhmolJffLsTDr9cLogSUso7roTT5g+07qz8cZKhDTMjWXcxMgNmx35cHd7VWzPOJaqaJGa7PrqazDRiporHnoCzqera3N9DZZ3TPtvy5Hp8pt/GtvIcf7uymKd88cz+P6yrRMTrx4wCbj8BWkNKMECiMeH/34sO26dnxD+5TcEjj+JCfG9APReYf+tONwbGO8LugJf+J43nH77y5nmf//qHj1sns2cFt084tlPT897/rZ/sFeyRh53/2osPhe1W9MH38DsDpc85lbq0Bgbr7obOhLsSMaeYwoGW5onf2U5RtpaNYr1ivVHDtrZtsQsEV5KKTdksHFk1iAc6/BLMK52sDYjIGHUAbxt3rRhy4Q015fzYpZ9RaTRpgAnqDoo7nx2hoGRsh54y7uzMotVEQkphgHddfbzSUKnQQAKNZDk3vVvPYseMY6bi3rd5EgClFlM2HtKThFGLOHMyybTbnIZNZ1th5K4FGohp/3xkirbrmPyfUhcBAievLsGPdIQRP/puJIbbeIBotI3Bsv0KE8icxArWEMfs45YxkgnwUSyO80C0TqbWx4U18LnQcRseF8Hw9+hr2QmyILixIifZosg1BHbpgNpViNmnwXZwtp4Rta3j79i3ePb4llGlSFOdTGu+YQ4roALZCSRDPxj3TaeoyFj5LJOPh4QG9u/Bd5uAac4qQfSaBBBlMSM+CWveuYhkT0FJKgyU5bIYe7RtG3cQh5A/tf5Ej5Pw+bPMc7j7aSSN92bkpe+PnqRWDvcdnp1aE36VHAmT0h5AxlfDyxSdYlunDtunZ8aNqCsBuXHfje/uZYJvEU8/bz34YIjouxiMc4797hHBEfCoSTZX4RTyDi3yzeSHou7KFm8+GI+eXx3OH8KFzHDMGDofZPzugpxitiCeDjSV6EDCzGoN/MzuUeYc7b5sFp2gshBjUhgB1SK/E87s5mtQsOqcxzZmQ0HzarAGK08NabxaJ2eS4XtCNCTGf79FrBQuZaURF/NO7Sj0l3d+XGubZVcewnOu6DQNMxhejwRSDRTNhcMo98vT6zDxPiPEFLpcLni5vRxDgxcDeGiW3j9mifcYduxMH/Nw+/J3vnj0OMIVP4dRRNDPOFBXcN5tj7KqC67Yi54x3j++QE2Wvt22l8zBsOASxsZGNxir4ZpcRQDr8USPl1UspBht6xEknR7zaKK3zhG29wuGbZNCIVkILtVbWAnySnbMERc3AHAM0tYxvHpIINK7+M8JVKUUjGjRjubGIzffEbl3vju/akY1WCwDZhP6S7am78x25/iGg1WrTCyvXhj3jY0aeckaKySAhOsCc8nDEzg7ivQobAJ1WfTC6rTVoZZQ9zQvuH2ZMeUZOt7OSuaqfoR0mEdN6G5ne0SY+t0H8Xdv3h88dIfjnQfM8B7TmcOttVtF7pxz/uuJ0Og2Hyu51r7EAvXaTHCnIkRDa5XLBDzl+dE3hOSyzS0LsFXAIGPnqbrj38ZSWZokZROWH3GQfHYMb1yOcs1NLb6/Lf/dD//tD13782bFQ8yEW03c5sz29BDzi4AaOh9/x6JByDk6hDJIwpiYpMy6quZKH7rOsvaHFv3cY5n7AI6EQjWiVYnMeeULJEkl5Qp8WDOVU0zdqpVCSoVTUUtB6QambQVArSt3Q68ao3Ngr6jmxiEVie0ZWrXNZexvPRzuGEJ0qhuP1TVFLJftnTiZgFwCj79VajNPeh3bQeZkGi0ltsWnHkL8IFnEGBVIMVhup6LAeA+xFTNU2FDFjihAlblsr+zdSyuTYB2LUtdso0iCYDsN3vI6goEwxZdEdAhRKm+dI1pMqvLGU74lF39YorhhDw9pIt/WhNbtEAYMfzqzwzI1ZwjzPUADbSopoTMnozMcaFuXD45z5OW07tu4KuLZ2glHC0al1JUKqI7rNMBca3WYjML3zN4SAslEjLAVvjAw4nU44zRMH7gjfUc4TWW6XC4XtAHa6G0SWYkbMVh/IdAQxBJtHkMZnQozGhDw0w4Hrkw1iZjvs3EEFOVMzaZ5nnM9naH8fUg7BRlse95nndMPAw97Vsf44rMvh3b1vP77LJnmAsNuhHWbqveO6XrCVDSHaeF9J5uyqqR/0EeidTifM84xSCgdV/YDjRzmF74Nz/MYHvU1ujfF7vz8gkl3/ZEAidhyjhL0Qs2cn3wsvfeBnz1+GG6Yd49ObtO931yK+29Ec4ahxBLFNtxdEtTtm745253P7EePeXOb4KeeeWEREoAMhBiTNQ++lW3EyxAw1BUjqxrMgndICVUU2XZqulMvwWdP8cyONs7aR3rNRSQdVFHCYwjIEx9rFaao6nsMOg1mxrHVsW4MqjX8pvObeK1z3h/dOyCDFCTE20/SHFYqtqa4yiJhPd9iuV2wrVVxZXAz2TDqC1U9GRNoIreQo0Mi+lhQi8sQh6xqoFxWsUBtCsgK9R6D7fZKZxUzKgxrH0xFoZD2rIQwB1EraY4gsWN/dPWCeJoPd9mzxOF+gW4NiihQNnOflBqYrpVgXsNWsAh1fnvJoGHS45Pad+MCmCJdkn6ZsWaeOc6/rOhwKQEPfLZvoWhCNSrtthCsBl03BMFrABarenZyRI3spnC6d4t49nGfKjMgw9Gk4COgepPH9CJlO3jsVPHAhlKm9D8qzT707BoPwfc2oddibborCvSu8XfV9u+K7to/P+zU/tyPPkZTjv4+xAn1/Zr1zTjM7t5sV3Tl4yWm5QdhH4g4h58SMLafvtZfH4x9VU/jwYdHbB1Kq578//k12uqf/zhFjvoWVbh3UBx+u2SDxFGQgCrcw1q1D8IaQW9kGH8rx/KXuaeOHeyK+CzLzBccPunM4XPfxg9hTT4/GCYXIsw+T6hdMokHUNmAISBNxxDFARZVF02HQYAU3Z7Yw2mABmQ1sUosxm4pxwcugPzJqb0MTp7cKUcV6ZTelj0N0iqo/k71Y6E1EjkNv2NYNrTeczwt2+e2GWjs2o/2mlJDTZIyTjBwIU4QYcf/wAuW64vXrr3F5fAuF4uH+HjFEvH39Glu5cmxiyphnYF2pOaWNBqErMBv0k+eEkDKaYmQMoxP1Jsvcg4De1Byq4bqqCNcVaZ4Qohe9g0WujPzniT0dOVEHR21EaLIB7603DmsCICaNLoHPttaOd2/fYT6dEELA5emKlAljxpysRyMDG+sCpZJqPM9cG+u6WeE3Gi3V1z9nY8zGPHM4z5kwTjUtW8W2MagQ6WPusctOuDCds/Uczkspj/uNlnnlnBGSDbEPOxc/5myaQ/5vNsHMxeC48QAhBVVivO0HERkS7A0MwnxdulifB2Mxea2AzB/fr67rVVvFVrhnXrx4wMPDi1G09qBxZ3yxoD8yj8N/biuO1wjcQtbugP1cW7kOKvD1eh3wmH++1YbtesU8z7i/vwMAlJUyHD90psI/iH00jCreF5bzYd7P06bnUfrx348NJh9yHrdY8U4dZnx2iLKBHc6yqF8P5ztejxv74z1xIxArdNhGbtLv3VA708ZTyecv+UOOsPvnzBC7kT8a+OEEQQgOx2cmHVEwvhfYf7cLswyPjGIQzisQIUTRXawLoC593qc3qUKijSzsdAa9dyRtmLUh9AK16KS1bnz5YmJqbTTScXZtYdPdVliIFTFYyVaMCFK0LlRgd4zwSAxmMEmbleDGpQLakSSgrGT2QCPHataOGMlD77Wg9Xd0RssJZVvxdHnC5bLidFqQlxkK3us0z6jF1nAAtssT1m1DV8VWG5oC9z1gPgF5PiHmiQVoJYQkHKjAclBvEIkGUXlBNJtBS+iqmOcFEEGpPr6TWeMOFThrijATs5kKaISTDPg5cOa1qhnXCdfrinVdTWV1ZgauOox3CIJ2ZeNgGswkTuSLMRhrZ8fePSgKwXShulNhCbmI7ANvrpcV27YhREHrrFfxOim6GGKwgr3j+8CcZ+Q8IaWJgoGJTCHKwXDdB6OWRtPJQsjAgGxlQEMAuN4P9xwCMwVVHYXigN2GiALaK/dGDChbJVNKFa07K2tXOWUNhqygrRRct8IaUFuwlSvfQ/ZAkoOZmE/ITe/S8XCn6lpVR9vhz9B1i7w3yt+nO1w2EDIAa/Y+5jzh888/HXNqRHcI/occP7rQzM37PmXzfYPuqZ6/LByM5zG630WvfmiKBd0dgf/suJC7le87SdOjMP1dxWw/QrCRkQgDPxTxFiirDQAG2fi1kuPslAEG9mJZ/56KAuAgeSseiZ8fgJieOk+uFuHsssQe0ftMAjoTp31SBiSM6+Q1hbBLRwtA+YhoDkbjULx0zHqPThJKJTccwjSb4hBW6GpqshwVojK48/uw9RV148jRUurYSN3GGJbWULXaO1FMKQHeYaycjT3N82AVBQnIMdr1ANeyofYK6QKUFboRHoqxITWO/Xx8R/qqaENZrwgdWC+Ek6JFjzknpBQpCJgiRCZEiQhhxXXbUEvH5bIhTRsUEQgZnCApo4M6RLHfEXQxA2CjVr0Y2hodQOsd18sTmUVtb5iapwUBrFFABK00PF1WKHiOFhpafUIUNgi6syCPneyo3n2Ep+Lu7g6n0wlvHx8hImi9o5WGogVlLYAC85nc+xQT1JzuEf4lk2wnc7Ta8FSaicmxBqMw6mRpe5etOb+UZsta7TlH73zPY53lmDFPy6jLeMMd623RtpexqnIycT8bxgQ57GO+T4eyjnucukADJjjizoSUNKDUykwlJ2joY52x0G1ikK0Ng5tzxhLYtHo6nxBjwOVywfl8xul84nPZKnd357qeYhgFazoN7on1erFRsYJlOSEnQj3RNcLMJkw5QeCDowIkKgISogCl2gREk4S5W0749NNPsYyRqTtd/oce/7hC8zCcRwyeF+LvYI8C3eu6cd7P9bvw/+ff7dQyXoN9Hta5ahS2Y6Hmtg6wL6IjzMPPAiIOc7hhdw152GbYTfl+3XITtzvMdVNpN9opSUajimLXtI/lg5/f5J85RW0X58NYXF749fewd6Mej6Oj9TMwUrrVbj8W9anpogNnlmC4qqfXvSH3DlSylxyaYi/Fht42eIPNcAoG+7hCZVfTze8scutQIW3mDASX64oUhPUJ0HinKaOLMtqujfMuFGiloicWHVEbLuWCKMIIPgj7G0SAGFmci15IDGjN3ydlkWNk5pDzjBgM0hBxjMI6nLnGmvSb5ygGCUExNmOtxbSWnLpJ2CPEhBwjyb62Rn3aWQzMOqJP35OOeZ5wuTS8e3fBul6xLDTu6LvAI2cj8H62jRnfNE0WSEUrY9gwG6PNPp/UVkpACOsON8JIFP6OD9F3ipRjjykipGiSInsnNWUs9lnF/l/OEyms3WsBJsMR2PU95kPHAEmJAonjPd128nqw6dd/hGx8z7fWoVZb2Qfa8/wxR9wv9yQktH1uQYwRpRj0ZTMYvBO6d0q8M1OueHx8hAI4nU/I0zymrRXTIDsqK/QYkF3Jtnc8PT3h8d3bkQVMpsHEeyQK0JvnOdQtSznxPHHPIkUo3nc+n3co/kCrP/aTfN/xjys0H/D655/1Px2mOf7uc1iJUa6+d47nxlzMwytkpIZMD8k26J1UsebqmrLXC/ycz897cwz8fr82/p7/8P3MaP/vtitxf0D7/fr85OFKDtf33Dnu33nM0t7vjDw2ID13pB+qaxxZE7fY/v5zV64czXjBjbVBUG4EpSHhMLGtdwAJ28piZIgBp+k0rs+dhCvV0uhUtMJB9NUklaGcL1xqgbaKph21URYbESY3LEg5YkpkpOQ8ASpYtw0///2v8O7pHS5Pj3h6eqRjyBNCoHy1iGArDbl2tE44h4qWFHNrnVPsFNSi78rJX5PNj26r3+t+X465l8LnhCTjufg8iZh9Mp2tXRvocrm8wTTNeLh/AdWO83kBh+M05OQibA0pxdG45Li+iJjD4Roim+aEWhseVYcBDCEgp4zeiEU7vn8kc/g7PHLuRfZ5wzFGhJwQjV2X7NrSmDNAqMz3UbBr8yBpx9aFzCJTbtUbuCoMxwKAEwYBM8Q4XNde6L+FXZoVsWGQWDCnsOPpQ2crxsHI8XpOxTYaLud5xjyTcdWMJTUk6FtDMbJFCNHe45MZ5vPt0JzafEOPZ+tB6TSxLna9Xke9zmsE/jPv4N4DuLaTTw42xLu3+TDEhl/p+K5/8prChyLr7zb0uPn3I+bu57rJAp59/kPH84fy3DgeF9XR2D3/3PPz39Q3nl3889rHh67p+b+7MRWxJOPw+99VY/mQARcZV3RzHLFH//sxbT5ex3ddpx7+7UPPe3cUZmziLpks2GcJI5hS46CIwmoBgsvlalEqmSuMsOvIKjy6UW1Ap9Jsrw29F6Cbzov9V63Ytq5XNNCYrZcrSq3Yygq1706Zapv/7O6ET7/4DKUW/Pa3v8XXX/8Wj4+PaK3h7v6Bs5/XKxveUkZIlBFQoSPzECUEnpNQEaDS0ao64RHqEuUW2Jggjk3pE0pog6q6TRnhMVq3zBMUiSuRYm8uf8wong1JRTBmSvTKrOq0LIcBQRhGVgFcr1crTLoWUB3XR6mMPhAVp28SbqQYHTNsNaNvuv8uEWFUUI9w2SyWEUIaSzWm5Ejq0AaT4HWiMIQLYVnzzrgOiDaLAbIXa6GCVilBncJtxnGEgo+Bn9c6OB0wDtu1s/f6WPsetNbeMOWMGBJCaONnIlS4LYeZF86W0rAXzwHKoGxbwTR15ByHkW6lmNKvjqzZ95gXjX2gz+VygXbg3bt3h2J+wvl8NzJ3H8XK+91ldRyJ6a2jjkBzr9Gk/E/cvPahY0AmN0bnWJD9QBqB3SDukT4+ZP/GZ48G3bX8j995TB2/q7L/3rU/M5SOk+IDxvWYhj6/rw85GdYHnmU4It4bdfPZcR7cOornPz9+34cM/vPPHq/1vetVvfFYw+E6D1sEbrVSSkOyRESgIY1X1YzbriGOBiIBm5hOp+Z3Nd5NzrtaYzPsvbbNoL+9R0O1G5xkeHVruFfKZde2M1pEFZNJXL95+xqff/E55RVCQNWO08MDvlwWLA8v8Nf/9a/w+vU3iNOCUise14rLxu/otaEW8vaDEAbpqlCJaBA0FRvXWIfEc0zTGBIjYsY/JMSJxT1q7yREVWACivVaMBIm1u3yCS9fvATA7GpbV5StcIZvUODq2S0j3+t6MWyZ0fS6XrFeVqhSL2uPnsk4g8BYOmkvVE471fN5xutGlNIk2QrrGFL27hRa6wYNTaNOCIjRfemwYnDlUwtasNNDtfssgGoYv2k2xX2qWQiCKc/G0GoDNvbneDxGrSLnwdpRy/DQMb47SiQM6amx7T6Hwb3+0SzwLKWgrFQDcAfAzHAeELU7Ctk4X911i0jrDUjzAhhdO8ZCWfFBiZ3gSrWq1F+qpeK6rnh8fIQItcJydsVhHVARi9/sEPd33rsOR+sif+4E8/RP7BQ8rTkWdIYh+YDxOkawz4/n3XxHhtDtufbmMH7fd0fn/l3Hc/v5jhH5h671u873ob8fN9CHzjMieSGk5L8PEPYYDg6U0x3nsuj7ZgPJ++d//jz9no+Z3Ic+c3O9ImNOr33AsOPDOc1ZM8rb6zAsqMJw7DCK+nt3MPHcmKbx3gCn/Pp72msQjL7ogLQ1E6AzSLA3yLaO72omWNdMe6jXSmwWiodXn+B8PiFPLHbmiWMw5zzjrIKHV5/gdH+PFAK26xUpnyhfUTZASSFFr2b4ohWEOwQRpXRslXLfqs4iU4jeYrQiu+yBF3JZHgqYlxMHwZuQGZ9px7Y9jRqOy25crk+geN4e0ap25CmTIuxrJnQoAk7n+/F+9ug8Hgw/9xFrKBHBZibs4ndckYym4zC6IQQ6BYs4Y9gzBYDNdTcia2I9HF5Tu6k32vs3qXWXguaQqD2Q8z10dEw7DNcHW+e43/33HFqbpskgoj2I4P2Gkf2ORrtSWF/yd9spKxEjja47ypzu7F4wxvFSbsTmt2hAT4DITrsF6JglxtGMmmOExDoYRRCBShhNrTA4b55mXC8rYkg2lW2/Zj+3OxJCjc/qrsaOO0LmH1Qq/cDxo6Wzj6kPs5K9CWwvJL/PJnovO8ABs+9qDJDbhrIjHOTfTUcko+nLF3UQgdws0N3Y9uZGOgC6S93yyw+LF7ANsMMx31e5Pxpbh4xGltK9sBXG93jLy829H7KgD9UAPvQenv/dn9uHPvMhyOr4Ox9y2r4R9t/xc+2yFopuek3BJIJBTSJgp/7JoWbkcNEw+JY9TDDqpdrkNhPqA4epTLXYZDvCNnwfCtj4TBYBN6QZQEyQNGNOwSiMLHxOi+KLn32FVniuWgt6qbvAXDAaaSO7A3KYKy0yBrorCHcobBKahFF89TXU1Tu3Aym1BqP4HeyMNj7XPFEGxAvfgODOunStt20cZOccM2QWpAUYdE831MN4hH1GgUCg4o2Guy6UZywhsjZDwkBlr0DKEJvrIEa+COKTCZ+JRorsoyTl+LMd72+tImXqagVtSDkPyFa7U3EBV9gdPR2BC8ppnM7b9/v02gk1ndj7AJyMKcc6D7NRQpx8GgFTZkG3QwfE5vi8gLpMOaURmPiecAntGCMEcbDFUj4qrVpA1I4acD7WN9uYXZey8LoczPnxmV8uF8wzhf6YSdAJ7bUi9vGwp6If9jT7ZTyLCMZ2+yHHP0j76HnkecwenstQPI/U/TgaQDcKx589j+535wBbeLdy3Te/dzR+Byjr9lqO12Dr0m3Zdxje4/G+Mb3NZHgdx2vCoMY+v+YPXR8AG/eJ3cHt/KG9LK+H//3smr/vT9jmtQoDXJYb5hzHnw4n4dBXoYoebEqdG3TdvzfePCsZm9GDhWPW1w1fH7CR7iyRUT/ynzlbC5wYp13RSkXZrsC60vB0QHtgU9Q8WyabkNKCbaUgYGodcgJyjIimS7ReVxbFzSm4Q3Dn2S2K7ML8VZWjILt1dwfTR3KIMwROYoNYRB09ej6uJ+vLOMIqqmx2M30ppyYeHX8wiqNHhPE9IxxsUQMC9lJ47aMru4kF+2xuj3anaWJdxrrXJQSTi5DRD8A1YDTvm7Vi61Nv73Gv9cnhGh0BsDndfBTMQnu3RNFx/b5LWqQ8sgvezm1NBMBgVAHefzFhnoLV0Y4O/HafNA9KzGFu22aZlXBQU99p+DEmKvlSBne/V7mFy/z8XXXM9tgdOudze5ObM/N8UpsqCR+cddEx2+Cpbs9n1DIO5+NcQwusHJU3zTIOW/on7lM4RswjkveBOjjg0kpZhuPx/CE9P3ZTdxs5H887FhhAjPBgRL+vdrAbt/1a/LxHQzn+/QPX8PxevuOLbheaPD+P3EBt31mLGM+B57Qz433aqj0b/5vozbmeX+vtdbtGE6+5qxVOvaYwvnPcENSjRPsTnfxqfOjZ+/cf/k7/sauYjihKd0qsN5Xx6IPe69mFP5Iggc1KALQ2XC4Rmyl/zjPnE3Ooik0yi4FDVyJlK2qtEBCjz2Zolq1AlNGvWTpzCvvG7m4EAHvebaihSghjRKTfAbMdHT0Zo840vgMjI4Pfq0E5MnD6/Vxqj5ZwBYaT9GwBZnC9qYsDr47BBq8npvmmWzjFhGAT0FJMkFChwtpR9/Me4MZdBdWOAUuIOcz399ZeNI3wTFS5GHi9CgTpg0jQTDbDIWRfFm70HWN3myMm0uewZAhOjrHvEGFEH/c1rjh0HoNqox747NPmIqDNstsdzhMRyEEtddimwz7zqN1Zh7h5NtxxR5qu/7zIriQbY8LNZMem6GjmPOjkIbxMNlPC9soBHRFCgO17zOTx+EcVmuVANTxGBUeM+0NGGLg16gIBZ7iKLSs+tNGp3C0tDcMcjvN8FzziPyM+/77RPf79xmA+iyCO9YoPfv7Z38dnILYx3ZGSb+zX/vwcQfa95dcccPuZ7zq8l+BD5/XjJhODw2R2uwcWijsZHx7z/BIIaeyRGWR3DGOTPruO8c0feObhkLGpmnMTxWgOHM9MbXF7QsOMJGQgpIw0nWzUYh7jNoMqIA2wBi1oxJQX5MnE+7qidCCnGef5jhO31L9PbtbCcArwaJnX1FszAUOm8YIw6jGAjWG0veHT5IazF9imb8N5hBC4GMQdP8Y74ZUZtGZwZTSlWJEDrBMtU+iwRki/F9s7ujtbh7o8K2gqdAhJgK4Q2bV/eM3RBNjGv5j/5L7taM92KP/WhcFi8Ot41lNjY5zHukopQW5E4XR09X5ojfvPgWM9k/+15pkKr1PH7wCtMVtzIblohtijd/+gf+P+/Qa9DYNOA328b0G0LMgNvuzz4gVj9nwIgTpO0gGlDZ2mGZy5HFFKxeVyxTxPTKTUVZMNfQiuJLHbv32+w4eRmu87flSfgr8wN/ohBDPmRwbM7QU8L3y+b1yPAnAHSql3VB4/q2aUvjNaf2aMdJ9T4H//UCF6XO8h8/i+DOT7HMTzn/tnRkr/Xb8DjOgfeBap47tf6Hi+H8g83iswv/dt2KPvGH/YovHz6e2/HR3ve07hWZb4PHNUdfaRG3+9caYmp0vXYKJyAttMEhBCRp5O41pa75xipx1aCwCxCAuAqIn5TQhTRBAvUE5Wo1CrGQjrALYBg7L4LuOiPFr08Yc7rbN1dwq7cN3eWDje9Lj/0STmxuzgkAcsI7c1oFG/iXGsccIXe6YSJxt8dHAIvCyu9XB4d74eOkCNJwlGKrBItDtN90PYtOxrewQWe0QOIarQeh1ZrYGRfDfHehj2oEgODsDX8rFP4EhD9WfjvROtMouT5FP1XFrG16jvm/35RpP59gB3XVfLpKK9QwYdLvTYboJTwGd83D6W3cb5++9GT3ZJE7V1UhvrXN6fUAr3A6m1PneCZ/G6kZN09mexP6uxBw+oww85flSmIIcL8C/u/dZZPP/ft5v/1tjy77c38l2R+Q5l4Ma4fghuOha4QzjCLvsCis8W9u9yNL/re7iwMH522BL86TAKt9/zHAJ77gR/6PHjoa7vv44PwVvjc+/fxvjr82wNAI6w4/HPAYUdTkjYZF9fEtxIstDMMPcQmVopxAMUT3/YFR3ZeBcUMgWk2LDVgpgipmVBytSQUSiQ4pBWFhhxAUBXykscAwoxI0dsPUKa89cJaUSbkCcIwymIHA3IiFWhqojptq9GVdkdPD4nI2jxzzhzq7aGnCbkxBGYzEj4+RjzyK6eBzn+v4+w8PMaXa8+VlVpfFQppHiAh33E6HDowOFeD8Yp7NmN/8w+vNfOcJCvORj645o67hO3L0eH4QwdkhOCDRfaG2i9N+P4ewKM2kk31COljBjZwZxiNKvPtRrE4NTeRwDkmZfDoR6n0U7stkZxcHrYGwV77xzUpB3nZRnFbopO9lFnEIuSmB0yE+noNwGDYUjjHrW/v9+/7xD9oTnFx+Pj8fH4eHw8/n/++GHl6I/Hx+Pj8fH4ePz/xfHRKXw8Ph4fj4/Hx2McH53Cx+Pj8fH4eHw8xvHRKXw8Ph4fj4/Hx2McH53Cx+Pj8fH4eHw8xvHRKXw8Ph4fj4/Hx2McH53Cx+Pj8fH4eHw8xvHRKXw8Ph4fj4/Hx2McH53Cx+Pj8fH4eHw8xvH/BUh08sph04BcAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot an image tensor\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(image)\n",
        "plt.title(class_names[label.numpy()]) # add title to image by indexing on class_names list\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04fa7e9a-9918-4bc0-8e0f-c87893922157",
      "metadata": {
        "id": "04fa7e9a-9918-4bc0-8e0f-c87893922157"
      },
      "source": [
        "Delicious!\n",
        "\n",
        "Okay, looks like the Food101 data we've got from TFDS is similar to the datasets we've been using in previous notebooks.\n",
        "\n",
        "Now let's preprocess it and get it ready for use with a neural network."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8e1e2538-2340-4394-8700-6a126f1b2faa",
      "metadata": {
        "id": "8e1e2538-2340-4394-8700-6a126f1b2faa"
      },
      "source": [
        "## Create preprocessing functions for our data\n",
        "\n",
        "In previous notebooks, when our images were in folder format we used the method [`tf.keras.utils.image_dataset_from_directory()`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory) to load them in.\n",
        "\n",
        "Doing this meant our data was loaded into a format ready to be used with our models.\n",
        "\n",
        "However, since we've downloaded the data from TensorFlow Datasets, there are a couple of preprocessing steps we have to take before it's ready to model. \n",
        "\n",
        "More specifically, our data is currently:\n",
        "\n",
        "* In `uint8` data type\n",
        "* Comprised of all differnet sized tensors (different sized images)\n",
        "* Not scaled (the pixel values are between 0 & 255)\n",
        "\n",
        "Whereas, models like data to be:\n",
        "\n",
        "* In `float32` data type\n",
        "* Have all of the same size tensors (batches require all tensors have the same shape, e.g. `(224, 224, 3)`)\n",
        "* Scaled (values between 0 & 1), also called normalized\n",
        "\n",
        "To take care of these, we'll create a `preprocess_img()` function which:\n",
        "\n",
        "* Resizes an input image tensor to a specified size using [`tf.image.resize()`](https://www.tensorflow.org/api_docs/python/tf/image/resize)\n",
        "* Converts an input image tensor's current datatype to `tf.float32` using [`tf.cast()`](https://www.tensorflow.org/api_docs/python/tf/cast)\n",
        "\n",
        "> 🔑 **Note:** Pretrained EfficientNetBX models in [`tf.keras.applications.efficientnet`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet) (what we're going to be using) have rescaling built-in. But for many other model architectures you'll want to rescale your data (e.g. get its values between 0 & 1). This could be incorporated inside your \"`preprocess_img()`\" function (like the one below) or within your model as a [`tf.keras.layers.Rescaling`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Rescaling) layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "51b7307e-165a-4509-8e99-ce484bfc39f8",
      "metadata": {
        "id": "51b7307e-165a-4509-8e99-ce484bfc39f8"
      },
      "outputs": [],
      "source": [
        "# Make a function for preprocessing images\n",
        "def preprocess_img(image, label, img_shape=224):\n",
        "    \"\"\"\n",
        "    Converts image datatype from 'uint8' -> 'float32' and reshapes image to\n",
        "    [img_shape, img_shape, color_channels]\n",
        "    \"\"\"\n",
        "    image = tf.image.resize(image, [img_shape, img_shape]) # reshape to img_shape\n",
        "    return tf.cast(image, tf.float32), label # return (float32_image, label) tuple"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "253aaffa-6722-46cc-9b4e-2b625cca84fc",
      "metadata": {
        "id": "253aaffa-6722-46cc-9b4e-2b625cca84fc"
      },
      "source": [
        "Our `preprocess_img()` function above takes image and label as input (even though it does nothing to the label) because our dataset is currently in the tuple structure `(image, label)`.\n",
        "\n",
        "Let's try our function out on a target image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "9cdb051f-6e67-4189-a69a-44748d4a7bd5",
      "metadata": {
        "id": "9cdb051f-6e67-4189-a69a-44748d4a7bd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6634b282-122e-4dc7-d450-a4bf34a92420"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image before preprocessing:\n",
            " [[[12 13  7]\n",
            "  [12 13  7]\n",
            "  [13 14  8]\n",
            "  ...\n",
            "  [21 11  0]\n",
            "  [21 11  0]\n",
            "  [21 11  0]]\n",
            "\n",
            " [[12 13  7]\n",
            "  [11 12  6]\n",
            "  [11 12  6]\n",
            "  ...\n",
            "  [21 11  0]\n",
            "  [21 11  0]\n",
            "  [21 11  0]]]...,\n",
            "Shape: (512, 512, 3),\n",
            "Datatype: <dtype: 'uint8'>\n",
            "\n",
            "Image after preprocessing:\n",
            " [[[11.586735   12.586735    6.586735  ]\n",
            "  [11.714286   12.714286    6.714286  ]\n",
            "  [ 8.857142    9.857142    4.8571424 ]\n",
            "  ...\n",
            "  [20.714308   11.142836    1.2857144 ]\n",
            "  [20.668371   10.668372    0.        ]\n",
            "  [21.         11.          0.        ]]\n",
            "\n",
            " [[ 2.3571415   3.3571415   0.1428566 ]\n",
            "  [ 3.1530607   4.153061    0.07653028]\n",
            "  [ 3.0561223   4.0561223   0.        ]\n",
            "  ...\n",
            "  [26.071407   18.071407    7.0714073 ]\n",
            "  [24.785702   14.785702    4.7857018 ]\n",
            "  [22.499966   12.499966    2.4999657 ]]]...,\n",
            "Shape: (224, 224, 3),\n",
            "Datatype: <dtype: 'float32'>\n"
          ]
        }
      ],
      "source": [
        "# Preprocess a single sample image and check the outputs\n",
        "preprocessed_img = preprocess_img(image, label)[0]\n",
        "print(f\"Image before preprocessing:\\n {image[:2]}...,\\nShape: {image.shape},\\nDatatype: {image.dtype}\\n\")\n",
        "print(f\"Image after preprocessing:\\n {preprocessed_img[:2]}...,\\nShape: {preprocessed_img.shape},\\nDatatype: {preprocessed_img.dtype}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2f56a9f-6d66-4c7a-9321-5831391a1e3e",
      "metadata": {
        "id": "b2f56a9f-6d66-4c7a-9321-5831391a1e3e"
      },
      "source": [
        "Excellent! Looks like our `preprocess_img()` function is working as expected.\n",
        "\n",
        "The input image gets converted from `uint8` to `float32` and gets reshaped from its current shape to `(224, 224, 3)`.\n",
        "\n",
        "How does it look?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "6046678b-0e38-40bf-b4c5-e0d4b0d98f97",
      "metadata": {
        "id": "6046678b-0e38-40bf-b4c5-e0d4b0d98f97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "f60c6424-00d6-4fb4-b7ff-e7dab2a827e8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# We can still plot our preprocessed image as long as we \n",
        "# divide by 255 (for matplotlib capatibility)\n",
        "plt.imshow(preprocessed_img/255.)\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3324aa01-f28e-4960-b5e5-0e245310f2d9",
      "metadata": {
        "id": "3324aa01-f28e-4960-b5e5-0e245310f2d9"
      },
      "source": [
        "All this food visualization is making me hungry. How about we start preparing to model it?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24a4982c-dc50-4aae-b823-9ac13c8d95b6",
      "metadata": {
        "id": "24a4982c-dc50-4aae-b823-9ac13c8d95b6"
      },
      "source": [
        "## Batch & prepare datasets\n",
        "\n",
        "Before we can model our data, we have to turn it into batches.\n",
        "\n",
        "Why?\n",
        "\n",
        "Because computing on batches is memory efficient.\n",
        "\n",
        "We turn our data from 101,000 image tensors and labels (train and test combined) into batches of 32 image and label pairs, thus enabling it to fit into the memory of our GPU.\n",
        "\n",
        "To do this in effective way, we're going to be leveraging a number of methods from the [`tf.data` API](https://www.tensorflow.org/api_docs/python/tf/data).\n",
        "\n",
        "> 📖 **Resource:** For loading data in the most performant way possible, see the TensorFlow docuemntation on [Better performance with the tf.data API](https://www.tensorflow.org/guide/data_performance).\n",
        "\n",
        "Specifically, we're going to be using:\n",
        "\n",
        "* [`map()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map) - maps a predefined function to a target dataset (e.g. `preprocess_img()` to our image tensors)\n",
        "* [`shuffle()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) - randomly shuffles the elements of a target dataset up `buffer_size` (ideally, the `buffer_size` is equal to the size of the dataset, however, this may have implications on memory)\n",
        "* [`batch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch) - turns elements of a target dataset into batches (size defined by parameter `batch_size`)\n",
        "* [`prefetch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch) - prepares subsequent batches of data whilst other batches of data are being computed on (improves data loading speed but costs memory)\n",
        "* Extra: [`cache()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#cache) - caches (saves them for later) elements in a target dataset, saving loading time (will only work if your dataset is small enough to fit in memory, standard Colab instances only have 12GB of memory) \n",
        "\n",
        "Things to note:\n",
        "- Can't batch tensors of different shapes (e.g. different image sizes, need to reshape images first, hence our `preprocess_img()` function)\n",
        "- `shuffle()` keeps a buffer of the number you pass it images shuffled, ideally this number would be all of the samples in your training set, however, if your training set is large, this buffer might not fit in memory (a fairly large number like 1000 or 10000 is usually suffice for shuffling)\n",
        "- For methods with the `num_parallel_calls` parameter available (such as `map()`), setting it to`num_parallel_calls=tf.data.AUTOTUNE` will parallelize preprocessing and significantly improve speed\n",
        "- Can't use `cache()` unless your dataset can fit in memory\n",
        "\n",
        "Woah, the above is alot. But once we've coded below, it'll start to make sense.\n",
        "\n",
        "We're going to through things in the following order:\n",
        "\n",
        "```\n",
        "Original dataset (e.g. train_data) -> map() -> shuffle() -> batch() -> prefetch() -> PrefetchDataset\n",
        "```\n",
        "\n",
        "This is like saying, \n",
        "\n",
        "> \"Hey, map this preprocessing function across our training dataset, then shuffle a number of elements before batching them together and make sure you prepare new batches (prefetch) whilst the model is looking through the current batch\".\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-prefetching-from-hands-on-ml.png)\n",
        "\n",
        "*What happens when you use prefetching (faster) versus what happens when you don't use prefetching (slower). **Source:** Page 422 of [Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow Book by Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/).*\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "15991d7f-b649-4a3b-b443-e61b56ce653c",
      "metadata": {
        "id": "15991d7f-b649-4a3b-b443-e61b56ce653c"
      },
      "outputs": [],
      "source": [
        "# Map preprocessing function to training data (and paralellize)\n",
        "train_data = train_data.map(map_func=preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "# Shuffle train_data and turn it into batches and prefetch it (load it faster)\n",
        "train_data = train_data.shuffle(buffer_size=1000).batch(batch_size=32).prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "# Map prepreprocessing function to test data\n",
        "test_data = test_data.map(preprocess_img, num_parallel_calls=tf.data.AUTOTUNE)\n",
        "# Turn test data into batches (don't need to shuffle)\n",
        "test_data = test_data.batch(32).prefetch(tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f504e9c7-6bf8-4fdb-acd4-c67abffbf091",
      "metadata": {
        "id": "f504e9c7-6bf8-4fdb-acd4-c67abffbf091"
      },
      "source": [
        "And now let's check out what our prepared datasets look like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "9b2f3c75-739c-4eb0-8156-a21dea3de937",
      "metadata": {
        "id": "9b2f3c75-739c-4eb0-8156-a21dea3de937",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9046036e-8882-4cb6-c711-82c0d3af52f8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>,\n",
              " <_PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "train_data, test_data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5781af7a-7faa-4fc6-b1e9-8b5db0d233b0",
      "metadata": {
        "id": "5781af7a-7faa-4fc6-b1e9-8b5db0d233b0"
      },
      "source": [
        "Excellent! Looks like our data is now in tutples of `(image, label)` with datatypes of `(tf.float32, tf.int64)`, just what our model is after.\n",
        "\n",
        "> 🔑 **Note:** You can get away without calling the `prefetch()` method on the end of your datasets, however, you'd probably see significantly slower data loading speeds when building a model. So most of your dataset input pipelines should end with a call to [`prefecth()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch).\n",
        "\n",
        "Onward."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "df30b415-ee22-4694-b7d2-dfe968e7bd10",
      "metadata": {
        "id": "df30b415-ee22-4694-b7d2-dfe968e7bd10"
      },
      "source": [
        "## Create modelling callbacks\n",
        "\n",
        "Since we're going to be training on a large amount of data and training could take a long time, it's a good idea to set up some modelling callbacks so we be sure of things like our model's training logs being tracked and our model being checkpointed (saved) after various training milestones.\n",
        "\n",
        "To do each of these we'll use the following callbacks:\n",
        "* [`tf.keras.callbacks.TensorBoard()`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard) - allows us to keep track of our model's training history so we can inspect it later (**note:** we've created this callback before have imported it from `helper_functions.py` as `create_tensorboard_callback()`)\n",
        "* [`tf.keras.callbacks.ModelCheckpoint()`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint) - saves our model's progress at various intervals so we can load it and resuse it later without having to retrain it\n",
        "  * Checkpointing is also helpful so we can start fine-tuning our model at a particular epoch and revert back to a previous state if fine-tuning offers no benefits"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "2f54a576-3892-4043-a500-9a6b95199dce",
      "metadata": {
        "id": "2f54a576-3892-4043-a500-9a6b95199dce"
      },
      "outputs": [],
      "source": [
        "# Create TensorBoard callback (already have \"create_tensorboard_callback()\" from a previous notebook)\n",
        "from helper_functions import create_tensorboard_callback\n",
        "\n",
        "# Create ModelCheckpoint callback to save model's progress\n",
        "checkpoint_path = \"model_checkpoints/cp.ckpt\" # saving weights requires \".ckpt\" extension\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n",
        "                                                      monitor=\"val_accuracy\", # save the model weights with best validation accuracy\n",
        "                                                      save_best_only=True, # only save the best weights\n",
        "                                                      save_weights_only=True, # only save model weights (not whole model)\n",
        "                                                      verbose=0) # don't print out whether or not model is being saved "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "04981d24-169e-4148-8b83-de19983a93ee",
      "metadata": {
        "id": "04981d24-169e-4148-8b83-de19983a93ee"
      },
      "source": [
        "## Setup mixed precision training\n",
        "\n",
        "We touched on mixed precision training above.\n",
        "\n",
        "However, we didn't quite explain it.\n",
        "\n",
        "Normally, tensors in TensorFlow default to the float32 datatype (unless otherwise specified).\n",
        "\n",
        "In computer science, float32 is also known as [single-precision floating-point format](https://en.wikipedia.org/wiki/Single-precision_floating-point_format). The 32 means it usually occupies 32 bits in computer memory.\n",
        "\n",
        "Your GPU has a limited memory, therefore it can only handle a number of float32 tensors at the same time.\n",
        "\n",
        "This is where mixed precision training comes in.\n",
        "\n",
        "Mixed precision training involves using a mix of float16 and float32 tensors to make better use of your GPU's memory.\n",
        "\n",
        "Can you guess what float16 means?\n",
        "\n",
        "Well, if you thought since float32 meant single-precision floating-point, you might've guessed float16 means [half-precision floating-point format](https://en.wikipedia.org/wiki/Half-precision_floating-point_format). And if you did, you're right! And if not, no trouble, now you know.\n",
        "\n",
        "For tensors in float16 format, each element occupies 16 bits in computer memory.\n",
        "\n",
        "So, where does this leave us?\n",
        "\n",
        "As mentioned before, when using mixed precision training, your model will make use of float32 and float16 data types to use less memory where possible and in turn run faster (using less memory per tensor means more tensors can be computed on simultaneously).\n",
        "\n",
        "As a result, using mixed precision training can improve your performance on modern GPUs (those with a compute capability score of 7.0+) by up to 3x.\n",
        "\n",
        "For a more detailed explanation, I encourage you to read through the [TensorFlow mixed precision guide](https://www.tensorflow.org/guide/mixed_precision) (I'd highly recommend at least checking out the summary).\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-mixed-precision-speedup-equals-3x-gpu.png)\n",
        "*Because mixed precision training uses a combination of float32 and float16 data types, you may see up to a 3x speedup on modern GPUs.*\n",
        "\n",
        "> 🔑 **Note:** If your GPU doesn't have a score of over 7.0+ (e.g. P100 in Google Colab), mixed precision won't work (see: [\"Supported Hardware\"](https://www.tensorflow.org/guide/mixed_precision#supported_hardware) in the mixed precision guide for more).\n",
        "\n",
        "> 📖 **Resource:** If you'd like to learn more about precision in computer science (the detail to which a numerical quantity is expressed by a computer), see the [Wikipedia page](https://en.wikipedia.org/wiki/Precision_(computer_science)) (and accompanying resources). \n",
        "\n",
        "Okay, enough talk, let's see how we can turn on mixed precision training in TensorFlow.\n",
        "\n",
        "The beautiful thing is, the [`tensorflow.keras.mixed_precision`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/) API has made it very easy for us to get started.\n",
        "\n",
        "First, we'll import the API and then use the [`set_global_policy()`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/set_global_policy) method to set the *dtype policy* to `\"mixed_float16\"`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "4a06d5fd-54e6-4220-a2f3-67445225e033",
      "metadata": {
        "id": "4a06d5fd-54e6-4220-a2f3-67445225e033"
      },
      "outputs": [],
      "source": [
        "# Turn on mixed precision training\n",
        "from tensorflow.keras import mixed_precision\n",
        "mixed_precision.set_global_policy(policy=\"mixed_float16\") # set global policy to mixed precision "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c5e49ae-4276-49f3-b736-f2479866e156",
      "metadata": {
        "id": "1c5e49ae-4276-49f3-b736-f2479866e156"
      },
      "source": [
        "Nice! As long as the GPU you're using has a compute capability of 7.0+ the cell above should run without error.\n",
        "\n",
        "Now we can check the global dtype policy (the policy which will be used by layers in our model) using the [`mixed_precision.global_policy()`](https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision/global_policy) method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "d45bd7e1-b5f1-4ffb-8bec-1f8281023510",
      "metadata": {
        "id": "d45bd7e1-b5f1-4ffb-8bec-1f8281023510",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc8a4745-2dd1-4a1e-e175-05d7b90bc42c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Policy \"mixed_float16\">"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "mixed_precision.global_policy() # should output \"mixed_float16\" (if your GPU is compatible with mixed precision)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e66df4cf-9daa-4ee2-8df0-cf29e667b5e4",
      "metadata": {
        "id": "e66df4cf-9daa-4ee2-8df0-cf29e667b5e4"
      },
      "source": [
        "Great, since the global dtype policy is now `\"mixed_float16\"` our model will automatically take advantage of float16 variables where possible and in turn speed up training."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a64ec4e-8774-4bdc-a5d2-f0a44f85d50e",
      "metadata": {
        "id": "1a64ec4e-8774-4bdc-a5d2-f0a44f85d50e"
      },
      "source": [
        "## Build feature extraction model\n",
        "\n",
        "Callbacks: ready to roll.\n",
        "\n",
        "Mixed precision: turned on.\n",
        "\n",
        "Let's build a model.\n",
        "\n",
        "Because our dataset is quite large, we're going to move towards fine-tuning an existing pretrained model (EfficienetNetB0).\n",
        "\n",
        "But before we get into fine-tuning, let's set up a feature-extraction model.\n",
        "\n",
        "Recall, the typical order for using transfer learning is:\n",
        "\n",
        "1. Build a feature extraction model (replace the top few layers of a pretrained model) \n",
        "2. Train for a few epochs with lower layers frozen\n",
        "3. Fine-tune if necessary with multiple layers unfrozen\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-feature-extraction-then-fine-tune.png)\n",
        "*Before fine-tuning, it's best practice to train a feature extraction model with custom top layers.*\n",
        "\n",
        "To build the feature extraction model (covered in [Transfer Learning in TensorFlow Part 1: Feature extraction](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/04_transfer_learning_in_tensorflow_part_1_feature_extraction.ipynb)), we'll:\n",
        "* Use `EfficientNetB0` from [`tf.keras.applications`](https://www.tensorflow.org/api_docs/python/tf/keras/applications) pre-trained on ImageNet as our base model\n",
        "  * We'll download this without the top layers using `include_top=False` parameter so we can create our own output layers\n",
        "* Freeze the base model layers so we can use the pre-learned patterns the base model has found on ImageNet\n",
        "* Put together the input, base model, pooling and output layers in a [Functional model](https://keras.io/guides/functional_api/)\n",
        "* Compile the Functional model using the Adam optimizer and [sparse categorical crossentropy](https://www.tensorflow.org/api_docs/python/tf/keras/losses/SparseCategoricalCrossentropy) as the loss function (since our labels **aren't** one-hot encoded)\n",
        "* Fit the model for 3 epochs using the TensorBoard and ModelCheckpoint callbacks\n",
        "\n",
        "> 🔑 **Note:** Since we're using mixed precision training, our model needs a separate output layer with a hard-coded `dtype=float32`, for example, `layers.Activation(\"softmax\", dtype=tf.float32)`. This ensures the outputs of our model are returned back to the float32 data type which is more numerically stable than the float16 datatype (important for loss calculations). See the [\"Building the model\"](https://www.tensorflow.org/guide/mixed_precision#building_the_model) section in the TensorFlow mixed precision guide for more.\n",
        "\n",
        "![](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/07-mixed-precision-code-before-and-after.png)\n",
        "*Turning mixed precision on in TensorFlow with 3 lines of code.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "7573b3d3-28bd-469b-a1c4-f19e780fce38",
      "metadata": {
        "id": "7573b3d3-28bd-469b-a1c4-f19e780fce38",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16b24f40-d6ed-401d-ca55-2d58babd7628"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16705208/16705208 [==============================] - 2s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras import layers\n",
        "\n",
        "# Create base model\n",
        "input_shape = (224, 224, 3)\n",
        "base_model = tf.keras.applications.EfficientNetB0(include_top=False)\n",
        "base_model.trainable = False # freeze base model layers\n",
        "\n",
        "# Create Functional model \n",
        "inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "# Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below\n",
        "# x = layers.Rescaling(1./255)(x)\n",
        "x = base_model(inputs, training=False) # set base_model to inference mode only\n",
        "x = layers.GlobalAveragePooling2D(name=\"pooling_layer\")(x)\n",
        "x = layers.Dense(len(class_names))(x) # want one output neuron per class \n",
        "# Separate activation of output layer so we can output float32 activations\n",
        "outputs = layers.Activation(\"softmax\", dtype=tf.float32, name=\"softmax_float32\")(x) \n",
        "model = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", # Use sparse_categorical_crossentropy when labels are *not* one-hot\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "79cdfae0-7229-4923-bf55-9529c2d09134",
      "metadata": {
        "id": "79cdfae0-7229-4923-bf55-9529c2d09134",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82f55e8e-f61f-4933-9019-0133ea30b20e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
            " )                           )                                   \n",
            "                                                                 \n",
            " pooling_layer (GlobalAvera  (None, 1280)              0         \n",
            " gePooling2D)                                                    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activatio  (None, 101)               0         \n",
            " n)                                                              \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4178952 (15.94 MB)\n",
            "Trainable params: 129381 (505.39 KB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Check out our model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26787fb7-3762-41de-b61e-f01b54412d00",
      "metadata": {
        "id": "26787fb7-3762-41de-b61e-f01b54412d00"
      },
      "source": [
        "## Checking layer dtype policies (are we using mixed precision?)\n",
        "\n",
        "Model ready to go!\n",
        "\n",
        "Before we said the mixed precision API will automatically change our layers' dtype policy's to whatever the global dtype policy is (in our case it's `\"mixed_float16\"`).\n",
        "\n",
        "We can check this by iterating through our model's layers and printing layer attributes such as `dtype` and `dtype_policy`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "2a2df5ac-c297-4049-b721-9967ea6be88f",
      "metadata": {
        "id": "2a2df5ac-c297-4049-b721-9967ea6be88f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fd90532-1e63-43c0-dad3-e63ca8b54805"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_layer True float32 <Policy \"float32\">\n",
            "efficientnetb0 False float32 <Policy \"mixed_float16\">\n",
            "pooling_layer True float32 <Policy \"mixed_float16\">\n",
            "dense True float32 <Policy \"mixed_float16\">\n",
            "softmax_float32 True float32 <Policy \"float32\">\n"
          ]
        }
      ],
      "source": [
        "# Check the dtype_policy attributes of layers in our model\n",
        "for layer in model.layers:\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy) # Check the dtype policy of layers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "48db7fea-fb9d-4cc1-8b78-072e1dbc93e8",
      "metadata": {
        "id": "48db7fea-fb9d-4cc1-8b78-072e1dbc93e8"
      },
      "source": [
        "Going through the above we see:\n",
        "* `layer.name` (str) : a layer's human-readable name, can be defined by the `name` parameter on construction\n",
        "* `layer.trainable` (bool) : whether or not a layer is trainable (all of our layers are trainable except the efficientnetb0 layer since we set it's `trainable` attribute to `False`\n",
        "* `layer.dtype` : the data type a layer stores its variables in\n",
        "* `layer.dtype_policy` : the data type a layer computes in\n",
        "\n",
        "> 🔑 **Note:** A layer can have a dtype of `float32` and a dtype policy of `\"mixed_float16\"` because it stores its variables (weights & biases) in `float32` (more numerically stable), however it computes in `float16` (faster).\n",
        "\n",
        "We can also check the same details for our model's base model.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "a1d186ff-d01d-46d3-a88e-6fc7a849865f",
      "metadata": {
        "id": "a1d186ff-d01d-46d3-a88e-6fc7a849865f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba48203a-8be8-4e4c-fe27-e6883259134d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 False float32 <Policy \"float32\">\n",
            "rescaling False float32 <Policy \"mixed_float16\">\n",
            "normalization False float32 <Policy \"mixed_float16\">\n",
            "rescaling_1 False float32 <Policy \"mixed_float16\">\n",
            "stem_conv_pad False float32 <Policy \"mixed_float16\">\n",
            "stem_conv False float32 <Policy \"mixed_float16\">\n",
            "stem_bn False float32 <Policy \"mixed_float16\">\n",
            "stem_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv False float32 <Policy \"mixed_float16\">\n",
            "block1a_bn False float32 <Policy \"mixed_float16\">\n",
            "block1a_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn False float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in model.layers[1].layers[:20]: # only check the first 20 layers to save output space\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "616b4059-5d78-4466-afe4-9d49df3646ff",
      "metadata": {
        "id": "616b4059-5d78-4466-afe4-9d49df3646ff"
      },
      "source": [
        "> 🔑 **Note:** The mixed precision API automatically causes layers which can benefit from using the `\"mixed_float16\"` dtype policy to use it. It also prevents layers which shouldn't use it from using it (e.g. the normalization layer at the start of the base model)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "17ebfec1-b7ad-4d38-b42d-b266d09650a2",
      "metadata": {
        "id": "17ebfec1-b7ad-4d38-b42d-b266d09650a2"
      },
      "source": [
        "## Fit the feature extraction model\n",
        "\n",
        "Now that's one good looking model. Let's fit it to our data shall we?\n",
        "\n",
        "Three epochs should be enough for our top layers to adjust their weights enough to our food image data.\n",
        "\n",
        "To save time per epoch, we'll also only validate on 15% of the test data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "c1b8f418-391f-4232-af76-102eac0b9d82",
      "metadata": {
        "id": "c1b8f418-391f-4232-af76-102eac0b9d82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6fc4053-93ba-44b7-8cba-2d2e5365ac2f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: training_logs/efficientnetb0_101_classes_all_data_feature_extract/20230519-022415\n",
            "Epoch 1/3\n",
            "2368/2368 [==============================] - 67s 22ms/step - loss: 1.7186 - accuracy: 0.5808 - val_loss: 1.1152 - val_accuracy: 0.7018\n",
            "Epoch 2/3\n",
            "2368/2368 [==============================] - 51s 21ms/step - loss: 1.1989 - accuracy: 0.6896 - val_loss: 1.0340 - val_accuracy: 0.7135\n",
            "Epoch 3/3\n",
            "2368/2368 [==============================] - 51s 21ms/step - loss: 1.0530 - accuracy: 0.7241 - val_loss: 0.9952 - val_accuracy: 0.7240\n"
          ]
        }
      ],
      "source": [
        "# Turn off all warnings except for errors\n",
        "tf.get_logger().setLevel('ERROR')\n",
        "\n",
        "# Fit the model with callbacks\n",
        "history_101_food_classes_feature_extract = model.fit(train_data, \n",
        "                                                     epochs=3,\n",
        "                                                     steps_per_epoch=len(train_data),\n",
        "                                                     validation_data=test_data,\n",
        "                                                     validation_steps=int(0.15 * len(test_data)),\n",
        "                                                     callbacks=[create_tensorboard_callback(\"training_logs\", \n",
        "                                                                                            \"efficientnetb0_101_classes_all_data_feature_extract\"),\n",
        "                                                                model_checkpoint])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "29280c9a-c828-4cf5-8753-8c1bfdced3c0",
      "metadata": {
        "id": "29280c9a-c828-4cf5-8753-8c1bfdced3c0"
      },
      "source": [
        "Nice, looks like our feature extraction model is performing pretty well. How about we evaluate it on the whole test dataset?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "f30fb985-9735-4cc6-8d61-63451fc73bce",
      "metadata": {
        "id": "f30fb985-9735-4cc6-8d61-63451fc73bce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42345bd0-5638-4cc0-c109-e2b79350dfac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 11s 14ms/step - loss: 0.9993 - accuracy: 0.7279\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9992507100105286, 0.7279207706451416]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Evaluate model (unsaved version) on whole test dataset\n",
        "results_feature_extract_model = model.evaluate(test_data)\n",
        "results_feature_extract_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a7f263b-5ae0-4d43-a216-97d868003590",
      "metadata": {
        "id": "4a7f263b-5ae0-4d43-a216-97d868003590"
      },
      "source": [
        "And since we used the `ModelCheckpoint` callback, we've got a saved version of our model in the `model_checkpoints` directory.\n",
        "\n",
        "Let's load it in and make sure it performs just as well."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "693c7376-892d-4525-83ce-8a82ec12df76",
      "metadata": {
        "id": "693c7376-892d-4525-83ce-8a82ec12df76"
      },
      "source": [
        "## Load and evaluate checkpoint weights\n",
        "\n",
        "We can load in and evaluate our model's checkpoints by:\n",
        "\n",
        "1. Recreating a new instance of our model called `created_model` by turning our original model creation code into a function called `create_model()`. \n",
        "2. Compiling our `created_model` with the same loss, optimizer and metrics as the original model (every time you create a new model, you must compile it).\n",
        "3. Calling the `load_weights()` method on our `created_model` and passing it the path to where our checkpointed weights are stored.\n",
        "4. Calling `evaluate()` on `created_model` with loaded weights and saving the results.\n",
        "5. Comparing the `created_model` results to our previous `model` results (these should be the exact same, if not very close).\n",
        "\n",
        "A reminder, checkpoints are helpful for when you perform an experiment such as fine-tuning your model. In the case you fine-tune your feature extraction model and find it doesn't offer any improvements, you can always revert back to the checkpointed version of your model.\n",
        "\n",
        "> **Note:** This section originally used the [`tf.keras.clone_model` method](https://www.tensorflow.org/api_docs/python/tf/keras/models/clone_model), however, due to several potential errors with that method, it changed to create a new model (rather than cloning) via a `create_model()` function. See the [discussion on the course GitHub](https://github.com/mrdbourke/tensorflow-deep-learning/discussions/550) for more."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Create a function to recreate the original model\n",
        "def create_model():\n",
        "  # Create base model\n",
        "  input_shape = (224, 224, 3)\n",
        "  base_model = tf.keras.applications.efficientnet.EfficientNetB0(include_top=False)\n",
        "  base_model.trainable = False # freeze base model layers\n",
        "\n",
        "  # Create Functional model \n",
        "  inputs = layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "  # Note: EfficientNetBX models have rescaling built-in but if your model didn't you could have a layer like below\n",
        "  # x = layers.Rescaling(1./255)(x)\n",
        "  x = base_model(inputs, training=False) # set base_model to inference mode only\n",
        "  x = layers.GlobalAveragePooling2D(name=\"pooling_layer\")(x)\n",
        "  x = layers.Dense(len(class_names))(x) # want one output neuron per class \n",
        "  # Separate activation of output layer so we can output float32 activations\n",
        "  outputs = layers.Activation(\"softmax\", dtype=tf.float32, name=\"softmax_float32\")(x) \n",
        "  model = tf.keras.Model(inputs, outputs)\n",
        "  \n",
        "  return model\n",
        "\n",
        "# 2. Create and compile a new version of the original model (new weights)\n",
        "created_model = create_model()\n",
        "created_model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "                      optimizer=tf.keras.optimizers.Adam(),\n",
        "                      metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Load the saved weights\n",
        "created_model.load_weights(checkpoint_path)\n",
        "\n",
        "# 4. Evaluate the model with loaded weights\n",
        "results_created_model_with_loaded_weights = created_model.evaluate(test_data)"
      ],
      "metadata": {
        "id": "95f3PBdfWwoX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2095e08-eb6e-44d3-849c-11c066911094"
      },
      "id": "95f3PBdfWwoX",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 15s 15ms/step - loss: 0.9993 - accuracy: 0.7279\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4132ed85-f9c8-4380-8d26-c1aedec44fe4",
      "metadata": {
        "id": "4132ed85-f9c8-4380-8d26-c1aedec44fe4"
      },
      "source": [
        "Our `created_model` with loaded weight's results should be very close to the feature extraction model's results (if the cell below errors, something went wrong)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "8f5e3324-ff9f-4ef3-a645-30001378df9b",
      "metadata": {
        "id": "8f5e3324-ff9f-4ef3-a645-30001378df9b"
      },
      "outputs": [],
      "source": [
        "# 5. Loaded checkpoint weights should return very similar results to checkpoint weights prior to saving\n",
        "import numpy as np\n",
        "assert np.isclose(results_feature_extract_model, results_created_model_with_loaded_weights).all(), \"Loaded weights results are not close to original model.\"  # check if all elements in array are close"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "003e3c86-b5b2-4b0c-b505-4c7595e02a7e",
      "metadata": {
        "id": "003e3c86-b5b2-4b0c-b505-4c7595e02a7e"
      },
      "source": [
        "Cloning the model preserves `dtype_policy`'s of layers (but doesn't preserve weights) so if we wanted to continue fine-tuning our `created_model`, we could and it would still use the mixed precision dtype policy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "b67712a5-53ad-44ce-be8b-efda79b67726",
      "metadata": {
        "id": "b67712a5-53ad-44ce-be8b-efda79b67726",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62fdfa88-27ab-45eb-8238-dd29724b1b80"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_2 False float32 <Policy \"float32\">\n",
            "rescaling_2 False float32 <Policy \"mixed_float16\">\n",
            "normalization_1 False float32 <Policy \"mixed_float16\">\n",
            "rescaling_3 False float32 <Policy \"mixed_float16\">\n",
            "stem_conv_pad False float32 <Policy \"mixed_float16\">\n",
            "stem_conv False float32 <Policy \"mixed_float16\">\n",
            "stem_bn False float32 <Policy \"mixed_float16\">\n",
            "stem_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv False float32 <Policy \"mixed_float16\">\n",
            "block1a_bn False float32 <Policy \"mixed_float16\">\n",
            "block1a_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn False float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in created_model.layers[1].layers[:20]: # check only the first 20 layers to save printing space\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3476a48-9518-4519-967c-3007580e4768",
      "metadata": {
        "id": "a3476a48-9518-4519-967c-3007580e4768"
      },
      "source": [
        "## Save the whole model to file\n",
        "\n",
        "We can also save the whole model using the [`save()`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#save) method.\n",
        "\n",
        "Since our model is quite large, you might want to save it to Google Drive (if you're using Google Colab) so you can load it in for use later.\n",
        "\n",
        "> 🔑 **Note:** Saving to Google Drive requires mounting Google Drive (go to Files -> Mount Drive)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "78b47170-a22c-4293-878b-3d360462bd59",
      "metadata": {
        "id": "78b47170-a22c-4293-878b-3d360462bd59"
      },
      "outputs": [],
      "source": [
        "# ## Saving model to Google Drive (optional)\n",
        "\n",
        "# # Create save path to drive \n",
        "# save_dir = \"drive/MyDrive/tensorflow_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision/\"\n",
        "# # os.makedirs(save_dir) # Make directory if it doesn't exist\n",
        "\n",
        "# # Save model\n",
        "# model.save(save_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7a028463-2218-4379-9608-d4a4078ce7cf",
      "metadata": {
        "id": "7a028463-2218-4379-9608-d4a4078ce7cf"
      },
      "source": [
        "We can also save it directly to our Google Colab instance.\n",
        "\n",
        "> 🔑 **Note:** Google Colab storage is ephemeral and your model will delete itself (along with any other saved files) when the Colab session expires."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "c65f5b3f-27a8-44d4-a3fa-52f1055f37f2",
      "metadata": {
        "id": "c65f5b3f-27a8-44d4-a3fa-52f1055f37f2"
      },
      "outputs": [],
      "source": [
        "# Save model locally (if you're using Google Colab, your saved model will Colab instance terminates)\n",
        "save_dir = \"07_efficientnetb0_feature_extract_model_mixed_precision\"\n",
        "model.save(save_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "006eef4e-b9e6-415c-b69e-1583ffc1d1d0",
      "metadata": {
        "id": "006eef4e-b9e6-415c-b69e-1583ffc1d1d0"
      },
      "source": [
        "And again, we can check whether or not our model saved correctly by loading it in and evaluating it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "927b1e9d-ebd9-47c4-b167-83950a2bd58d",
      "metadata": {
        "id": "927b1e9d-ebd9-47c4-b167-83950a2bd58d"
      },
      "outputs": [],
      "source": [
        "# Load model previously saved above\n",
        "loaded_saved_model = tf.keras.models.load_model(save_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1579c008-0fb0-48a2-85b0-a5ee0e97a697",
      "metadata": {
        "id": "1579c008-0fb0-48a2-85b0-a5ee0e97a697"
      },
      "source": [
        "Loading a `SavedModel` also retains all of the underlying layers `dtype_policy` (we want them to be `\"mixed_float16\"`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "0afb18ec-2fdf-470f-b853-63104b0271c3",
      "metadata": {
        "id": "0afb18ec-2fdf-470f-b853-63104b0271c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "765ad70e-1869-470f-ea1f-2aa9f0a742b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 True float32 <Policy \"float32\">\n",
            "rescaling False float32 <Policy \"mixed_float16\">\n",
            "normalization False float32 <Policy \"mixed_float16\">\n",
            "rescaling_1 False float32 <Policy \"mixed_float16\">\n",
            "stem_conv_pad False float32 <Policy \"mixed_float16\">\n",
            "stem_conv False float32 <Policy \"mixed_float16\">\n",
            "stem_bn False float32 <Policy \"mixed_float16\">\n",
            "stem_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv False float32 <Policy \"mixed_float16\">\n",
            "block1a_bn False float32 <Policy \"mixed_float16\">\n",
            "block1a_activation False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand False float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv False float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv False float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn False float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in loaded_saved_model.layers[1].layers[:20]: # check only the first 20 layers to save output space\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "fd05f8f5-3470-40ae-a754-e644e2c8e5e4",
      "metadata": {
        "id": "fd05f8f5-3470-40ae-a754-e644e2c8e5e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a17ef35-aef5-43e3-a049-e3f79875adb0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 15s 16ms/step - loss: 0.9993 - accuracy: 0.7279\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9992507696151733, 0.7279207706451416]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "# Check loaded model performance (this should be the same as results_feature_extract_model)\n",
        "results_loaded_saved_model = loaded_saved_model.evaluate(test_data)\n",
        "results_loaded_saved_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "8ebde33b-380a-4656-94bb-a1c7cb1db452",
      "metadata": {
        "id": "8ebde33b-380a-4656-94bb-a1c7cb1db452"
      },
      "outputs": [],
      "source": [
        "# The loaded model's results should equal (or at least be very close) to the model's results prior to saving\n",
        "# Note: this will only work if you've instatiated results variables \n",
        "import numpy as np\n",
        "assert np.isclose(results_feature_extract_model, results_loaded_saved_model).all()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "916caf08-193e-43b3-ad80-b783b33ecb5a",
      "metadata": {
        "id": "916caf08-193e-43b3-ad80-b783b33ecb5a"
      },
      "source": [
        "That's what we want! Our loaded model performing as it should.\n",
        "\n",
        "> 🔑 **Note:** We spent a fair bit of time making sure our model saved correctly because training on a lot of data can be time-consuming, so we want to make sure we don't have to continaully train from scratch."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "359b51f5-2f97-42d8-835c-d84cef3c6966",
      "metadata": {
        "id": "359b51f5-2f97-42d8-835c-d84cef3c6966"
      },
      "source": [
        "## Preparing our model's layers for fine-tuning\n",
        "\n",
        "Our feature-extraction model is showing some great promise after three epochs. But since we've got so much data, it's probably worthwhile that we see what results we can get with fine-tuning (fine-tuning usually works best when you've got quite a large amount of data).\n",
        "\n",
        "Remember our goal of beating the [DeepFood paper](https://arxiv.org/pdf/1606.05675.pdf)?\n",
        "\n",
        "They were able to achieve 77.4% top-1 accuracy on Food101 over 2-3 days of training.\n",
        "\n",
        "Do you think fine-tuning will get us there?\n",
        "\n",
        "Let's find out.\n",
        "\n",
        "To start, let's load in our saved model.\n",
        "\n",
        "> 🔑 **Note:** It's worth remembering a traditional workflow for fine-tuning is to freeze a pre-trained base model and then train only the output layers for a few iterations so their weights can be updated inline with your custom data (feature extraction). And then unfreeze a number or all of the layers in the base model and continue training until the model stops improving.\n",
        "\n",
        "Like all good cooking shows, I've saved a model I prepared earlier (the feature extraction model from above) to Google Storage.\n",
        "\n",
        "We can download it to make sure we're using the same model going forward."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "6d135faa-3ad7-4502-80b8-ea0f931f0c5c",
      "metadata": {
        "id": "6d135faa-3ad7-4502-80b8-ea0f931f0c5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7da5afc1-943e-48e5-a6f1-2d252a0fead0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-19 02:28:24--  https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.4.128, 142.251.10.128, 142.251.12.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.4.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16976857 (16M) [application/zip]\n",
            "Saving to: ‘07_efficientnetb0_feature_extract_model_mixed_precision.zip’\n",
            "\n",
            "07_efficientnetb0_f 100%[===================>]  16.19M  8.80MB/s    in 1.8s    \n",
            "\n",
            "2023-05-19 02:28:27 (8.80 MB/s) - ‘07_efficientnetb0_feature_extract_model_mixed_precision.zip’ saved [16976857/16976857]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download the saved model from Google Storage\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_feature_extract_model_mixed_precision.zip "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "4b09f43f-b0cd-4300-8605-67ecde8e5181",
      "metadata": {
        "id": "4b09f43f-b0cd-4300-8605-67ecde8e5181",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a84930d6-4144-42d4-d87d-4e9654ffa278"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  07_efficientnetb0_feature_extract_model_mixed_precision.zip\n",
            "   creating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/\n",
            "   creating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/variables/\n",
            "  inflating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/variables/variables.data-00000-of-00001  \n",
            "  inflating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/variables/variables.index  \n",
            "  inflating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/saved_model.pb  \n",
            "   creating: downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision/assets/\n"
          ]
        }
      ],
      "source": [
        "# Unzip the SavedModel downloaded from Google Stroage\n",
        "!mkdir downloaded_gs_model # create new dir to store downloaded feature extraction model\n",
        "!unzip 07_efficientnetb0_feature_extract_model_mixed_precision.zip -d downloaded_gs_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "402dc7fb-2ea5-4b5e-8c13-08e5589f8940",
      "metadata": {
        "id": "402dc7fb-2ea5-4b5e-8c13-08e5589f8940"
      },
      "outputs": [],
      "source": [
        "# Load and evaluate downloaded GS model\n",
        "loaded_gs_model = tf.keras.models.load_model(\"downloaded_gs_model/07_efficientnetb0_feature_extract_model_mixed_precision\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "1978476b-9d4f-4b87-8448-4cebad39c51a",
      "metadata": {
        "id": "1978476b-9d4f-4b87-8448-4cebad39c51a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62e97e95-8065-4ac6-f3df-f57ba3ccce70"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
            " )                           )                                   \n",
            "                                                                 \n",
            " pooling_layer (GlobalAvera  (None, 1280)              0         \n",
            " gePooling2D)                                                    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activatio  (None, 101)               0         \n",
            " n)                                                              \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4178952 (15.94 MB)\n",
            "Trainable params: 129381 (505.39 KB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Get a summary of our downloaded model\n",
        "loaded_gs_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "59acbfcd-8bb0-4528-8265-71400cf488af",
      "metadata": {
        "id": "59acbfcd-8bb0-4528-8265-71400cf488af"
      },
      "source": [
        "And now let's make sure our loaded model is performing as expected."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "cb87cb50-36d1-408d-b606-83ebab34fa9c",
      "metadata": {
        "id": "cb87cb50-36d1-408d-b606-83ebab34fa9c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c63b688-c083-407a-d8d7-1236b0e69680"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 15s 16ms/step - loss: 1.0881 - accuracy: 0.7067\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0880972146987915, 0.7066534757614136]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# How does the loaded model perform?\n",
        "results_loaded_gs_model = loaded_gs_model.evaluate(test_data)\n",
        "results_loaded_gs_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27177391-7647-46e9-bfa6-f5ebd934d524",
      "metadata": {
        "id": "27177391-7647-46e9-bfa6-f5ebd934d524"
      },
      "source": [
        "Great, our loaded model is performing as expected.\n",
        "\n",
        "When we first created our model, we froze all of the layers in the base model by setting `base_model.trainable=False` but since we've loaded in our model from file, let's check whether or not the layers are trainable or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "9cf5be86-4539-4e19-aef6-1ae66d50f2cb",
      "metadata": {
        "id": "9cf5be86-4539-4e19-aef6-1ae66d50f2cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1deb2c6-5547-4bd3-8862-3a99e2040be4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_layer True float32 <Policy \"float32\">\n",
            "efficientnetb0 True float32 <Policy \"mixed_float16\">\n",
            "pooling_layer True float32 <Policy \"mixed_float16\">\n",
            "dense True float32 <Policy \"mixed_float16\">\n",
            "softmax_float32 True float32 <Policy \"float32\">\n"
          ]
        }
      ],
      "source": [
        "# Are any of the layers in our model frozen?\n",
        "for layer in loaded_gs_model.layers:\n",
        "    layer.trainable = True # set all layers to trainable\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy) # make sure loaded model is using mixed precision dtype_policy (\"mixed_float16\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "af719483-1dbc-44ba-91a1-9cf0b69c52ca",
      "metadata": {
        "id": "af719483-1dbc-44ba-91a1-9cf0b69c52ca"
      },
      "source": [
        "Alright, it seems like each layer in our loaded model is trainable. But what if we got a little deeper and inspected each of the layers in our base model?\n",
        "\n",
        "> 🤔 **Question:** *Which layer in the loaded model is our base model?*\n",
        "\n",
        "Before saving the Functional model to file, we created it with five layers (layers below are 0-indexed):\n",
        "0. The input layer\n",
        "1. The pre-trained base model layer ([`tf.keras.applications.efficientnet.EfficientNetB0`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/efficientnet/EfficientNetB0))\n",
        "2. The pooling layer\n",
        "3. The fully-connected (dense) layer\n",
        "4. The output softmax activation (with float32 dtype)\n",
        "\n",
        "Therefore to inspect our base model layer, we can access the `layers` attribute of the layer at index 1 in our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "c767b7fb-36da-401d-afcb-c701b748e16c",
      "metadata": {
        "id": "c767b7fb-36da-401d-afcb-c701b748e16c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2ed90c9-8333-4fdf-f59a-0306192ee914"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_1 True float32 <Policy \"float32\">\n",
            "rescaling True float32 <Policy \"mixed_float16\">\n",
            "normalization True float32 <Policy \"float32\">\n",
            "stem_conv_pad True float32 <Policy \"mixed_float16\">\n",
            "stem_conv True float32 <Policy \"mixed_float16\">\n",
            "stem_bn True float32 <Policy \"mixed_float16\">\n",
            "stem_activation True float32 <Policy \"mixed_float16\">\n",
            "block1a_dwconv True float32 <Policy \"mixed_float16\">\n",
            "block1a_bn True float32 <Policy \"mixed_float16\">\n",
            "block1a_activation True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_squeeze True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reshape True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_reduce True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_expand True float32 <Policy \"mixed_float16\">\n",
            "block1a_se_excite True float32 <Policy \"mixed_float16\">\n",
            "block1a_project_conv True float32 <Policy \"mixed_float16\">\n",
            "block1a_project_bn True float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_conv True float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_bn True float32 <Policy \"mixed_float16\">\n",
            "block2a_expand_activation True float32 <Policy \"mixed_float16\">\n"
          ]
        }
      ],
      "source": [
        "# Check the layers in the base model and see what dtype policy they're using\n",
        "for layer in loaded_gs_model.layers[1].layers[:20]:\n",
        "    print(layer.name, layer.trainable, layer.dtype, layer.dtype_policy)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9a41092a-c1fc-4aad-aa95-5f4ec45346c2",
      "metadata": {
        "id": "9a41092a-c1fc-4aad-aa95-5f4ec45346c2"
      },
      "source": [
        "Wonderful, it looks like each layer in our base model is trainable (unfrozen) and every layer which should be using the dtype policy `\"mixed_policy16\"` is using it.\n",
        "\n",
        "Since we've got so much data (750 images x 101 training classes = 75750 training images), let's keep all of our base model's layers unfrozen.\n",
        "\n",
        "> 🔑 **Note:** If you've got a small amount of data (less than 100 images per class), you may want to only unfreeze and fine-tune a small number of layers in the base model at a time. Otherwise, you risk overfitting."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "caef02fc-bd6a-4dd5-85b9-b28a3b20aede",
      "metadata": {
        "id": "caef02fc-bd6a-4dd5-85b9-b28a3b20aede"
      },
      "source": [
        "## A couple more callbacks\n",
        "\n",
        "We're about to start fine-tuning a deep learning model with over 200 layers using over 100,000 (75k+ training, 25K+ testing) images, which means our model's training time is probably going to be much longer than before.\n",
        "\n",
        "> 🤔 **Question:** *How long does training take?*\n",
        "\n",
        "It could be a couple of hours or in the case of the [DeepFood paper](https://arxiv.org/pdf/1606.05675.pdf) (the baseline we're trying to beat), their best performing model took 2-3 days of training time.\n",
        "\n",
        "You will really only know how long it'll take once you start training.\n",
        "\n",
        "> 🤔 **Question:** *When do you stop training?*\n",
        "\n",
        "Ideally, when your model stops improving. But again, due to the nature of deep learning, it can be hard to know when exactly a model will stop improving.\n",
        "\n",
        "Luckily, there's a solution: the [`EarlyStopping` callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping).\n",
        "\n",
        "The `EarlyStopping` callback monitors a specified model performance metric (e.g. `val_loss`) and when it stops improving for a specified number of epochs, automatically stops training. \n",
        "\n",
        "Using the `EarlyStopping` callback combined with the `ModelCheckpoint` callback saving the best performing model automatically, we could keep our model training for an unlimited number of epochs until it stops improving.\n",
        "\n",
        "Let's set both of these up to monitor our model's `val_loss`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "bc2668c2-2406-46a5-a325-331234c64335",
      "metadata": {
        "id": "bc2668c2-2406-46a5-a325-331234c64335"
      },
      "outputs": [],
      "source": [
        "# Setup EarlyStopping callback to stop training if model's val_loss doesn't improve for 3 epochs\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", # watch the val loss metric\n",
        "                                                  patience=3) # if val loss decreases for 3 epochs in a row, stop training\n",
        "\n",
        "# Create ModelCheckpoint callback to save best model during fine-tuning\n",
        "checkpoint_path = \"fine_tune_checkpoints/\"\n",
        "model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n",
        "                                                      save_best_only=True,\n",
        "                                                      monitor=\"val_loss\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f0d3270a-393d-450d-9d8c-90abf2667738",
      "metadata": {
        "id": "f0d3270a-393d-450d-9d8c-90abf2667738"
      },
      "source": [
        "Woohoo! Fine-tuning callbacks ready.\n",
        "\n",
        "If you're planning on training large models, the `ModelCheckpoint` and `EarlyStopping` are two callbacks you'll want to become very familiar with. \n",
        "\n",
        "We're almost ready to start fine-tuning our model but there's one more callback we're going to implement: [`ReduceLROnPlateau`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ReduceLROnPlateau).\n",
        "\n",
        "Remember how the learning rate is the most important model hyperparameter you can tune? (if not, treat this as a reminder).\n",
        "\n",
        "Well, the `ReduceLROnPlateau` callback helps to tune the learning rate for you.\n",
        "\n",
        "Like the `ModelCheckpoint` and `EarlyStopping` callbacks, the `ReduceLROnPlateau` callback montiors a specified metric and when that metric stops improving, it reduces the learning rate by a specified factor (e.g. divides the learning rate by 10).\n",
        "\n",
        "> 🤔 **Question:** *Why lower the learning rate?*\n",
        "\n",
        "Imagine having a coin at the back of the couch and you're trying to grab with your fingers. \n",
        "\n",
        "Now think of the learning rate as the size of the movements your hand makes towards the coin. \n",
        "\n",
        "The closer you get, the smaller you want your hand movements to be, otherwise the coin will be lost.\n",
        "\n",
        "Our model's ideal performance is the equivalent of grabbing the coin. So as training goes on and our model gets closer and closer to it's ideal performance (also called **convergence**), we want the amount it learns to be less and less.\n",
        "\n",
        "To do this we'll create an instance of the `ReduceLROnPlateau` callback to monitor the validation loss just like the `EarlyStopping` callback. \n",
        "\n",
        "Once the validation loss stops improving for two or more epochs, we'll reduce the learning rate by a factor of 5 (e.g. `0.001` to `0.0002`).\n",
        "\n",
        "And to make sure the learning rate doesn't get too low (and potentially result in our model learning nothing), we'll set the minimum learning rate to `1e-7`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "cb2d24c1-7909-43da-bb27-5e6035806bb4",
      "metadata": {
        "id": "cb2d24c1-7909-43da-bb27-5e6035806bb4"
      },
      "outputs": [],
      "source": [
        "# Creating learning rate reduction callback\n",
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",  \n",
        "                                                 factor=0.2, # multiply the learning rate by 0.2 (reduce by 5x)\n",
        "                                                 patience=2,\n",
        "                                                 verbose=1, # print out when learning rate goes down \n",
        "                                                 min_lr=1e-7)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "53f03fb9-b8b2-403b-b4e8-2a7934631a1e",
      "metadata": {
        "id": "53f03fb9-b8b2-403b-b4e8-2a7934631a1e"
      },
      "source": [
        "Learning rate reduction ready to go!\n",
        "\n",
        "Now before we start training, we've got to recompile our model.\n",
        "\n",
        "We'll use sparse categorical crossentropy as the loss and since we're fine-tuning, we'll use a 10x lower learning rate than the Adam optimizers default (`1e-4` instead of `1e-3`). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "e8fffadf-49f9-447c-ac44-ad59f5e07e25",
      "metadata": {
        "id": "e8fffadf-49f9-447c-ac44-ad59f5e07e25"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "loaded_gs_model.compile(loss=\"sparse_categorical_crossentropy\", # sparse_categorical_crossentropy for labels that are *not* one-hot\n",
        "                        optimizer=tf.keras.optimizers.Adam(0.0001), # 10x lower learning rate than the default\n",
        "                        metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7fd557de-314b-4627-a0fd-f50e0bdb7ee7",
      "metadata": {
        "id": "7fd557de-314b-4627-a0fd-f50e0bdb7ee7"
      },
      "source": [
        "Okay, model compiled.\n",
        "\n",
        "Now let's fit it on all of the data. \n",
        "\n",
        "We'll set it up to run for up to 100 epochs. \n",
        "\n",
        "Since we're going to be using the `EarlyStopping` callback, it might stop before reaching 100 epochs.\n",
        "\n",
        "> 🔑 **Note:** Running the cell below will set the model up to fine-tune all of the pre-trained weights in the base model on all of the Food101 data. Doing so with **unoptimized** data pipelines and **without** mixed precision training will take a fairly long time per epoch depending on what type of GPU you're using (about 15-20 minutes on Colab GPUs). But don't worry, **the code we've written above will ensure it runs much faster** (more like 4-5 minutes per epoch)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "514dc1e0-0899-4831-9442-d007a8f4782b",
      "metadata": {
        "id": "514dc1e0-0899-4831-9442-d007a8f4782b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a20ee39-f2d2-4594-ba5c-14d22b60555f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to: training_logs/efficientb0_101_classes_all_data_fine_tuning/20230519-022854\n",
            "Epoch 1/100\n",
            "2368/2368 [==============================] - 246s 81ms/step - loss: 0.9223 - accuracy: 0.7525 - val_loss: 0.7872 - val_accuracy: 0.7749 - lr: 1.0000e-04\n",
            "Epoch 2/100\n",
            "2368/2368 [==============================] - 191s 81ms/step - loss: 0.5795 - accuracy: 0.8399 - val_loss: 0.7839 - val_accuracy: 0.7831 - lr: 1.0000e-04\n",
            "Epoch 3/100\n",
            "2368/2368 [==============================] - 162s 68ms/step - loss: 0.3299 - accuracy: 0.9063 - val_loss: 0.8827 - val_accuracy: 0.7765 - lr: 1.0000e-04\n",
            "Epoch 4/100\n",
            "2368/2368 [==============================] - ETA: 0s - loss: 0.1722 - accuracy: 0.9486\n",
            "Epoch 4: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
            "2368/2368 [==============================] - 162s 68ms/step - loss: 0.1722 - accuracy: 0.9486 - val_loss: 0.9571 - val_accuracy: 0.7850 - lr: 1.0000e-04\n",
            "Epoch 5/100\n",
            "2368/2368 [==============================] - 162s 68ms/step - loss: 0.0359 - accuracy: 0.9920 - val_loss: 1.0549 - val_accuracy: 0.8032 - lr: 2.0000e-05\n"
          ]
        }
      ],
      "source": [
        "# Start to fine-tune (all layers)\n",
        "history_101_food_classes_all_data_fine_tune = loaded_gs_model.fit(train_data,\n",
        "                                                        epochs=100, # fine-tune for a maximum of 100 epochs\n",
        "                                                        steps_per_epoch=len(train_data),\n",
        "                                                        validation_data=test_data,\n",
        "                                                        validation_steps=int(0.15 * len(test_data)), # validation during training on 15% of test data\n",
        "                                                        callbacks=[create_tensorboard_callback(\"training_logs\", \"efficientb0_101_classes_all_data_fine_tuning\"), # track the model training logs\n",
        "                                                                   model_checkpoint, # save only the best model during training\n",
        "                                                                   early_stopping, # stop model after X epochs of no improvements\n",
        "                                                                   reduce_lr]) # reduce the learning rate after X epochs of no improvements"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d58b2ebd-1955-416c-a76d-ec0999a25736",
      "metadata": {
        "id": "d58b2ebd-1955-416c-a76d-ec0999a25736"
      },
      "source": [
        "> 🔑 **Note:** If you didn't use mixed precision or use techniques such as [`prefetch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch) in the *Batch & prepare datasets* section, your model fine-tuning probably takes up to 2.5-3x longer per epoch (see the output below for an example).\n",
        "\n",
        "| | Prefetch and mixed precision | No prefetch and no mixed precision |\n",
        "|-----|-----|-----|\n",
        "| Time per epoch | ~280-300s | ~1127-1397s |\n",
        "\n",
        "*Results from fine-tuning 🍔👁 Food Vision Big™ on Food101 dataset using an EfficienetNetB0 backbone using a Google Colab Tesla T4 GPU.*\n",
        "\n",
        "```\n",
        "Saving TensorBoard log files to: training_logs/efficientB0_101_classes_all_data_fine_tuning/20200928-013008\n",
        "Epoch 1/100\n",
        "2368/2368 [==============================] - 1397s 590ms/step - loss: 1.2068 - accuracy: 0.6820 - val_loss: 1.1623 - val_accuracy: 0.6894\n",
        "Epoch 2/100\n",
        "2368/2368 [==============================] - 1193s 504ms/step - loss: 0.9459 - accuracy: 0.7444 - val_loss: 1.1549 - val_accuracy: 0.6872\n",
        "Epoch 3/100\n",
        "2368/2368 [==============================] - 1143s 482ms/step - loss: 0.7848 - accuracy: 0.7838 - val_loss: 1.0402 - val_accuracy: 0.7142\n",
        "Epoch 4/100\n",
        "2368/2368 [==============================] - 1127s 476ms/step - loss: 0.6599 - accuracy: 0.8149 - val_loss: 0.9599 - val_accuracy: 0.7373\n",
        "```\n",
        "*Example fine-tuning time for non-prefetched data as well as non-mixed precision training (~2.5-3x longer per epoch).*\n",
        "\n",
        "Let's make sure we save our model before we start evaluating it."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00ce480d-7f0a-40fc-b42e-3d28cfadd462",
      "metadata": {
        "id": "00ce480d-7f0a-40fc-b42e-3d28cfadd462"
      },
      "source": [
        "From the above, does it look like our model is overfitting or underfitting? \n",
        "\n",
        "Remember, if the training loss is significantly lower than the validation loss, it's a hint that the model has overfit the training data and not learned generalizable patterns to unseen data.\n",
        "\n",
        "But it does look like our model has gained a few performance points from fine-tuning, let's evaluate on the whole test dataset and see if managed to beat the [DeepFood paper's](https://arxiv.org/abs/1606.05675) result of 77.4% accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "483515ad-3ae8-4bf9-b484-fef56b87ed4c",
      "metadata": {
        "id": "483515ad-3ae8-4bf9-b484-fef56b87ed4c"
      },
      "outputs": [],
      "source": [
        "# # Save model to Google Drive (optional)\n",
        "# loaded_gs_model.save(\"/content/drive/MyDrive/tensorflow_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "578aea68-5351-4fea-a4b4-eeedb511c71b",
      "metadata": {
        "id": "578aea68-5351-4fea-a4b4-eeedb511c71b"
      },
      "outputs": [],
      "source": [
        "# Save model locally (note: if you're using Google Colab and you save your model locally, it will be deleted when your Google Colab session ends)\n",
        "loaded_gs_model.save(\"07_efficientnetb0_fine_tuned_101_classes_mixed_precision\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "312cb4c9-e399-4efb-8258-828a3f9291f9",
      "metadata": {
        "id": "312cb4c9-e399-4efb-8258-828a3f9291f9"
      },
      "source": [
        "## Download fine-tuned model from Google Storage\n",
        "\n",
        "As mentioned before, training models can take a significant amount of time.\n",
        "\n",
        "And again, like any good cooking show, here's something we prepared earlier...\n",
        "\n",
        "It's a fine-tuned model exactly like the one we trained above but it's saved to Google Storage so it can be accessed, imported and evaluated."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "325e55b2-dee4-4e3f-9113-82c9e749efbb",
      "metadata": {
        "id": "325e55b2-dee4-4e3f-9113-82c9e749efbb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4484f0f3-1127-4246-89e8-a9f8f242421c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-05-19 02:44:48--  https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.4.128, 142.251.10.128, 142.251.12.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.4.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 46790356 (45M) [application/zip]\n",
            "Saving to: ‘07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip’\n",
            "\n",
            "07_efficientnetb0_f 100%[===================>]  44.62M  14.1MB/s    in 3.2s    \n",
            "\n",
            "2023-05-19 02:44:51 (14.1 MB/s) - ‘07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip’ saved [46790356/46790356]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Download and evaluate fine-tuned model from Google Storage\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c5f856e-e58f-4575-b725-ca7b5004af10",
      "metadata": {
        "id": "1c5f856e-e58f-4575-b725-ca7b5004af10"
      },
      "source": [
        "The downloaded model comes in zip format (`.zip`) so we'll unzip it into the Google Colab instance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "29b9f6ba-1d0b-4043-a55d-40bef03d816b",
      "metadata": {
        "id": "29b9f6ba-1d0b-4043-a55d-40bef03d816b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e5a516a-7f60-4783-e3ce-06f0dd649592"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  07_efficientnetb0_fine_tuned_101_classes_mixed_precision.zip\n",
            "   creating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/\n",
            "   creating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/variables/\n",
            "  inflating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/variables/variables.data-00000-of-00001  \n",
            "  inflating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/variables/variables.index  \n",
            "  inflating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/saved_model.pb  \n",
            "   creating: downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision/assets/\n"
          ]
        }
      ],
      "source": [
        "# Unzip fine-tuned model\n",
        "!mkdir downloaded_fine_tuned_gs_model # create separate directory for fine-tuned model downloaded from Google Storage\n",
        "!unzip 07_efficientnetb0_fine_tuned_101_classes_mixed_precision -d downloaded_fine_tuned_gs_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "584eb626-33ea-489e-be8a-ba8517035688",
      "metadata": {
        "id": "584eb626-33ea-489e-be8a-ba8517035688"
      },
      "source": [
        "Now we can load it using the [`tf.keras.models.load_model()`](https://www.tensorflow.org/tutorials/keras/save_and_load) method and get a summary (it should be the exact same as the model we created above)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c0f85d6-3613-48bf-8ce1-fb6b58865545",
      "metadata": {
        "id": "1c0f85d6-3613-48bf-8ce1-fb6b58865545"
      },
      "outputs": [],
      "source": [
        "# Load in fine-tuned model from Google Storage and evaluate\n",
        "loaded_fine_tuned_gs_model = tf.keras.models.load_model(\"downloaded_fine_tuned_gs_model/07_efficientnetb0_fine_tuned_101_classes_mixed_precision\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "861798eb-0a34-4d91-ae7a-0b0b78561fdf",
      "metadata": {
        "id": "861798eb-0a34-4d91-ae7a-0b0b78561fdf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55b58742-53e8-4265-e7c1-2386350df88d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " efficientnetb0 (Functional  (None, None, None, 1280   4049571   \n",
            " )                           )                                   \n",
            "                                                                 \n",
            " pooling_layer (GlobalAvera  (None, 1280)              0         \n",
            " gePooling2D)                                                    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 101)               129381    \n",
            "                                                                 \n",
            " softmax_float32 (Activatio  (None, 101)               0         \n",
            " n)                                                              \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4178952 (15.94 MB)\n",
            "Trainable params: 4136929 (15.78 MB)\n",
            "Non-trainable params: 42023 (164.16 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Get a model summary (same model architecture as above)\n",
        "loaded_fine_tuned_gs_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "235fa9a8-ba2e-484e-aad2-dc2c00855934",
      "metadata": {
        "id": "235fa9a8-ba2e-484e-aad2-dc2c00855934"
      },
      "source": [
        "Finally, we can evaluate our model on the test data (this requires the `test_data` variable to be loaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "2276cadc-c4b2-4e9a-b7b6-15746ea6eb20",
      "metadata": {
        "id": "2276cadc-c4b2-4e9a-b7b6-15746ea6eb20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ea451e1-21a8-465c-e75d-a8b576fe70ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "790/790 [==============================] - 15s 16ms/step - loss: 0.9072 - accuracy: 0.8017\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9072489738464355, 0.801663339138031]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "# Note: Even if you're loading in the model from Google Storage, you will still need to load the test_data variable for this cell to work\n",
        "results_downloaded_fine_tuned_gs_model = loaded_fine_tuned_gs_model.evaluate(test_data)\n",
        "results_downloaded_fine_tuned_gs_model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "92419d08-3803-43bd-b588-9359e4a720d9",
      "metadata": {
        "id": "92419d08-3803-43bd-b588-9359e4a720d9"
      },
      "source": [
        "Excellent! Our saved model is performing as expected (better results than the DeepFood paper!).\n",
        "\n",
        "Congrautlations! You should be excited! You just trained a computer vision model with competitive performance to a research paper and in far less time (our model took ~20 minutes to train versus DeepFood's quoted 2-3 days).\n",
        "\n",
        "In other words, you brought Food Vision life!\n",
        "\n",
        "If you really wanted to step things up, you could try using the [`EfficientNetB4`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetB4) model (a larger version of `EfficientNetB0`). At at the time of writing, the EfficientNet family has the [state of the art classification results](https://paperswithcode.com/sota/fine-grained-image-classification-on-food-101) on the Food101 dataset.\n",
        "\n",
        "> 📖 **Resource:** To see which models are currently performing the best on a given dataset or problem type as well as the latest trending machine learning research, be sure to check out [paperswithcode.com](http://paperswithcode.com/) and [sotabench.com](https://sotabench.com/)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "24b6846d-f542-47b8-979b-65215bd12a74",
      "metadata": {
        "id": "24b6846d-f542-47b8-979b-65215bd12a74"
      },
      "source": [
        "## View training results on TensorBoard\n",
        "\n",
        "Since we tracked our model's fine-tuning training logs using the `TensorBoard` callback, let's upload them and inspect them on TensorBoard.dev."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "8bf8e3fb-1169-4160-8486-9f4a50a1ab4e",
      "metadata": {
        "id": "8bf8e3fb-1169-4160-8486-9f4a50a1ab4e"
      },
      "outputs": [],
      "source": [
        "# Upload experiment results to TensorBoard (uncomment to run)\n",
        "# !tensorboard dev upload --logdir ./training_logs \\\n",
        "#   --name \"Fine-tuning EfficientNetB0 on all Food101 Data\" \\\n",
        "#   --description \"Training results for fine-tuning EfficientNetB0 on Food101 Data with learning rate 0.0001\" \\\n",
        "#   --one_shot"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3c9e3985-0626-456c-9d45-e5b3134eb5e5",
      "metadata": {
        "id": "3c9e3985-0626-456c-9d45-e5b3134eb5e5"
      },
      "source": [
        "Viewing at our [model's training curves on TensorBoard.dev](https://tensorboard.dev/experiment/2KINdYxgSgW2bUg7dIvevw/), it looks like our fine-tuning model gains boost in performance but starts to overfit as training goes on.\n",
        "\n",
        "See the training curves on TensorBoard.dev here: https://tensorboard.dev/experiment/2KINdYxgSgW2bUg7dIvevw/\n",
        "\n",
        "To fix this, in future experiments, we might try things like:\n",
        "* A different iteration of `EfficientNet` (e.g. `EfficientNetB4` instead of `EfficientNetB0`).\n",
        "* Unfreezing less layers of the base model and training them rather than unfreezing the whole base model in one go."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "d5052bf8-29d4-48c0-8314-3c9ab6cb6aef",
      "metadata": {
        "id": "d5052bf8-29d4-48c0-8314-3c9ab6cb6aef"
      },
      "outputs": [],
      "source": [
        "# View past TensorBoard experiments\n",
        "# !tensorboard dev list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "f132dd1e-653f-46b6-9adb-e084bac84a84",
      "metadata": {
        "id": "f132dd1e-653f-46b6-9adb-e084bac84a84"
      },
      "outputs": [],
      "source": [
        "# Delete past TensorBoard experiments\n",
        "# !tensorboard dev delete --experiment_id YOUR_EXPERIMENT_ID\n",
        "\n",
        "# Example\n",
        "# !tensorboard dev delete --experiment_id OAE6KXizQZKQxDiqI3cnUQ"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d86fa758-b1bf-4113-88f7-c3d2996b93f5",
      "metadata": {
        "id": "d86fa758-b1bf-4113-88f7-c3d2996b93f5"
      },
      "source": [
        "## 🛠 Exercises \n",
        "\n",
        "1. Use the same evaluation techniques on the large-scale Food Vision model as you did in the previous notebook ([Transfer Learning Part 3: Scaling up](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/06_transfer_learning_in_tensorflow_part_3_scaling_up.ipynb)). More specifically, it would be good to see:\n",
        "  * A confusion matrix between all of the model's predictions and true labels.\n",
        "  * A graph showing the f1-scores of each class.\n",
        "  * A visualization of the model making predictions on various images and comparing the predictions to the ground truth.\n",
        "    * For example, plot a sample image from the test dataset and have the title of the plot show the prediction, the prediction probability and the ground truth label. \n",
        "2. Take 3 of your own photos of food and use the Food Vision model to make predictions on them. How does it go? Share your images/predictions with the other students.\n",
        "3. Retrain the model (feature extraction and fine-tuning) we trained in this notebook, except this time use [`EfficientNetB4`](https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetB4) as the base model instead of `EfficientNetB0`. Do you notice an improvement in performance? Does it take longer to train? Are there any tradeoffs to consider?\n",
        "4. Name one important benefit of mixed precision training, how does this benefit take place?"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f78edd4a-9363-4816-a828-36d9c6ff23d4",
      "metadata": {
        "id": "f78edd4a-9363-4816-a828-36d9c6ff23d4"
      },
      "source": [
        "## 📖 Extra-curriculum\n",
        "\n",
        "* Read up on learning rate scheduling and the [learning rate scheduler callback](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler). What is it? And how might it be helpful to this project?\n",
        "* Read up on TensorFlow data loaders ([improving TensorFlow data loading performance](https://www.tensorflow.org/guide/data_performance)). Is there anything we've missed? What methods you keep in mind whenever loading data in TensorFlow? Hint: check the summary at the bottom of the page for a gret round up of ideas.\n",
        "* Read up on the documentation for [TensorFlow mixed precision training](https://www.tensorflow.org/guide/mixed_precision). What are the important things to keep in mind when using mixed precision training?"
      ]
    }
  ],
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      "nbconvert_exporter": "python",
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    "colab": {
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      "machine_shape": "hm",
      "gpuType": "A100"
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    "gpuClass": "standard"
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